To see the other types of publications on this topic, follow the link: Bioinformatics Ontology.
Dissertations / Theses on the topic 'Bioinformatics Ontology'
Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles
Consult the top 50 dissertations / theses for your research on the topic 'Bioinformatics Ontology.'
Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.
You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.
Browse dissertations / theses on a wide variety of disciplines and organise your bibliography correctly.
1
Pohl, Matin. "Using an ontology to enhance metabolic or signaling pathway comparisions by biological and chemical knowledge." Thesis, University of Skövde, School of Humanities and Informatics, 2006. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-32.
Full textAbstract:
<p>Motivation:</p><p>As genome-scale efforts are ongoing to investigate metabolic networks of miscellaneous organisms the amount of pathway data is growing. Simultaneously an increasing amount of gene expression data from micro arrays becomes available for reverse engineering, delivering e.g. hypothetical regulatory pathway data. To avoid outgrowing of data and keep control of real new informations the need of analysis tools arises. One vital task is the comparison of pathways for detection of similar functionalities, overlaps, or in case of reverse engineering, detection of known data corroborating a hypothetical pathway. A comparison method using ontological knowledge about molecules and reactions will feature a more biological point of view which graph theoretical approaches missed so far. Such a comparison attempt based on an ontology is described in this report.</p><p>Results:</p><p>An algorithm is introduced that performs a comparison of pathways component by component. The method was performed on two selected databases and the results proved it to be not satisfying using it as stand-alone method. Further development possibilities are suggested and steps toward an integrated method using several approaches are recommended.</p><p>Availability:</p><p>The source code, used database snapshots and pictures can be requested from the author.</p>
2
Bergman, Laurila Jonas. "Ontology Slice Generation and Alignment for Enhanced Life Science Literature Search." Thesis, Linköping University, Linköping University, Linköping University, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-16440.
Full textAbstract:
<p>Query composition is an often complicated and cumbersome task for persons performing a literature search. This thesis is part of a project which aims to present possible queries to the user in form of natural language expressions. The thesis presents methods of ontology slice generation. Slices are parts of ontologies connecting two concepts along all possible paths between them. Those slices hence represent all relevant queries connecting the concepts and the paths can in a later step be translated into natural language expressions. Methods of slice alignment, connecting slices that originate from different ontologies, are also presented. The thesis concludes with some example scenarios and comparisons to related work.</p>
3
Wu, Xi. "Ontology-driven Web-based Medical Image Sharing Interface for Epilepsy Research." Case Western Reserve University School of Graduate Studies / OhioLINK, 2017. http://rave.ohiolink.edu/etdc/view?acc_num=case1496660866436638.
Full text
4
Helgadóttir, Hanna Sigrún. "Using semantic similarity measures across Gene Ontology to predict protein-protein interactions." Thesis, University of Skövde, School of Humanities and Informatics, 2005. http://urn.kb.se/resolve?urn=urn:nbn:se:his:diva-971.
Full textAbstract:
<p>Living cells are controlled by proteins and genes that interact through complex molecular pathways to achieve a specific function. Therefore, determination of protein-protein interaction is fundamental for the understanding of the cell’s lifecycle and functions. The function of a protein is also largely determined by its interactions with other proteins. The amount of protein-protein interaction data available has multiplied by the emergence of large-scale technologies for detecting them, but the drawback of such measures is the relatively high amount of noise present in the data. It is time consuming to experimentally determine protein-protein interactions and therefore the aim of this project is to create a computational method that predicts interactions with high sensitivity and specificity. Semantic similarity measures were applied across the Gene Ontology terms assigned to proteins in S. cerevisiae to predict protein-protein interactions. Three semantic similarity measures were tested to see which one performs best in predicting such interactions. Based on the results, a method that predicts function of proteins in connection with connectivity was devised. The results show that semantic similarity is a useful measure for predicting protein-protein interactions.</p>
5
Kusnierczyk, Waclaw. "Augmenting Bioinformatics Research with Biomedical Ontologies." Doctoral thesis, Norwegian University of Science and Technology, Faculty of Information Technology, Mathematics and Electrical Engineering, 2008. http://urn.kb.se/resolve?urn=urn:nbn:no:ntnu:diva-2001.
Full textAbstract:
<p>The main objective of the reported study was to investigate how biomedical ontologies, logically structured representations of various aspects of the biomedical reality, can help researchers in analyzing experimental data. The dissertation reports two attempts to construct tools for the analysis of high-throughput experimental results using explicit domain knowledge representations. Furthermore, integrative efforts made by the community of Open Biomedical Ontologies (OBO), in which the author has participated, are reported, and a framework for consistently connecting the Gene Ontology (GO) with the Taxonomy of Species is proposed and discussed.</p>
6
Abdulahad, Bassam, and Georgios Lounis. "A user interface for the ontology merging tool SAMBO." Thesis, Linköping University, Department of Computer and Information Science, 2004. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-2659.
Full textAbstract:
<p>Ontologies have become an important tool for representing data in a structured manner. Merging ontologies allows for the creation of ontologies that later can be composed into larger ontologies as well as for recognizing patterns and similarities between ontologies. Ontologies are being used nowadays in many areas, including bioinformatics. In this thesis, we present a desktop version of SAMBO, a system for merging ontologies that are represented in the languages OWL and DAML+OIL. The system has been developed in the programming language JAVA with JDK (Java Development Kit) 1.4.2. The user can open a file locally or from the network and can merge ontologies using suggestions generated by the SAMBO algorithm. SAMBO provides a user-friendly graphical interface, which guides the user through the merging process.</p>
7
Kandasamy, Meenakshi. "Approaches to Creating Fuzzy Concept Lattices and an Application to Bioinformatics Annotations." Miami University / OhioLINK, 2010. http://rave.ohiolink.edu/etdc/view?acc_num=miami1293821656.
Full text
8
Wynden, Rob. "The Health Ontology Mapper (HOM) Method Semantic Interoperability at Scale." Thesis, University of California, San Francisco, 2013. http://pqdtopen.proquest.com/#viewpdf?dispub=3587911.
Full textAbstract:
<p> The Health Ontology Mapper (HOM) method is a proposed solution to the semantic gap problem. The HOM Method provides the following functionality to enable the scalable deployment of informatics systems involving data from multiple health systems. The HOM method allows a relatively small population of biomedical ontology experts to describe the interpretation and analysis of biomedical information collected at thousands of hospitals via a cloud based terminology server. As such the HOM Method is focused on the scalability of the human talent required for successful informatics projects. The HOM promotes a means of converting UML based medical data into OWL format via a cloud-based method of controlling the data loading process. HOM subscribes to a means of converting data into a HIPAA Limited Data Set format to lower the risk associated with developing large virtual data repositories. HOM also provides a means of allowing access to medical data over grid computing environments by translating all information via a centralized web-based terminology server technology. </p><p> An integrated data repository (IDR) containing aggregations of clinical, biomedical, economic, administrative, and public health data is a key component of research infrastructure, quality improvement and decision support. But most available medical data is encoded using standard data warehouse architecture that employs arbitrary data encoding standards, making queries across disparate repositories difficult. In response to these shortcomings the Health Ontology Mapper (HOM) translates terminologies into formal data encoding standards without altering the underlying source data. The HOM method promotes inter-institutional data sharing and research collaboration, and will ultimately lower the barrier to developing and using an IDR.</p>
9
Espinosa, Octavio. "Characterisation of a mouse gene-phenotype network." Thesis, University of Oxford, 2011. http://ora.ox.ac.uk/objects/uuid:6231b62c-3047-46fc-a986-9f0565d4386b.
Full textAbstract:
Following advancements in the "omics" fields of molecular biology and genetics, much attention has been focused on categorising and annotating the large volume of data that has been produced since the sequencing of human and model genomes. With high-throughput data generated from these "omics" experiments and the increasing deposition of information from genetics experiments in biological databases, our understanding of the mechanisms that bridge the gap from genotype to phenotype can be explored in a holistic context. This is one of the aims of the relatively new field of systems biology, which aims to understand the complexity of biological systems in a holistic manner by studying the system as an ensemble of interacting parts. With increased volume and comprehensiveness of biological data, prediction of gene function and automatic identification of potential models for human diseases have become important aspects of systems-level analysis for wet-lab geneticists and clinicians. Here, I describe an integrated analysis of mouse phenotype data with high-throughput experiments to give genome-wide information about gene relationships and their function in a systems biology context. I show a functional dissection of mouse gene and phenotype networks and investigate the potential that ontology-compliant phenotype annotations can offer for functional classification of genes. The mouse genome and phenome show modularity at higher levels of cellular, physiological and organismal function. Using high-throughput protein-protein interaction data, the mouse proteome was dissected and computationally extracted communities were used to predict phenotypes of mouse gene ablation. Precision and recall curves show comparable performance for higher levels of the MP ontology to those undertaken by comprehensive mouse gene function prediction such as the Mouse Function Project which predicted Gene Ontology terms. I also developed and tested an automatic procedure that relates mouse phenotypes to human diseases and demonstrate its application to the use cases of identifying mouse models given a query consisting of a set of mouse phenotypes and breaking down human diseases into mouse phenotypes. Taken together, my results may be useful as a map for candidate gene discovery, finding how mouse networks relate to human networks and investigating the evolutionary origins of their components at higher levels of gene function.
10
Jain, Vishal. "Integrative approaches to modelling and knowledge discovery of molecular interactions in bioinformatics." Click here to access this resource online, 2008. http://hdl.handle.net/10292/439.
Full textAbstract:
The core focus of this research lies in developing and using intelligent methods to solve biological problems and integrating the knowledge for understanding the complex gene regulatory phenomenon. We have developed an integrative framework and used it to: model molecular interactions from separate case studies on time-series gene expression microarray datasets, molecular sequences and structure data including the functional role of microRNAs; to extract knowledge; and to build reusable models for the central dogma theme. Knowledge was integrated with the use of ontology and it can be reused to facilitate new discoveries as demonstrated on one of our systems – the Brain Gene Ontology (BGO). The central dogma theme states that proteins are produced from the DNA (gene) via an intermediate transcript called RNA. Later these proteins play the role of enzymes to perform the checkpoints as a gene expression control. Also, according to the recently emerged paradigm, sometimes genes do not code for proteins but results in small molecules of microRNAs which in turn controls the gene regulation. The idea is that such a very complicated molecular biology process (central dogma) results in production of a wide variety of data that can be used by computer scientists for modelling and to enable discoveries. We have suggested that this range of data should actually be taken into account for analysis to understand the concept of gene regulation instead of just taking one source of data and applying some standard methods to reveal facts in the system biology. The problem is very complex and, currently, computational algorithms have not been really successful because either existing methods have certain problems or the proven results were obtained for only one domain of the central dogma of molecular biology, so there has always been a lack of knowledge integration. Proper maintenance of diverse sources of data, structures and, in particular, their adaptation to new knowledge is one of the most challenging problems and one of the crucial tasks towards the knowledge integration vision is the efficient encoding of human knowledge in ontologies. More specifically this work has contributed towards the development of novel computational and information science methods and we have promoted the vision of knowledge integration by developing brain gene ontology (BGO) system. With the integrative use of several bioinformatics methods, this research has indeed resulted in modelling of such knowledge that has not been revealed in system biology so far. There are many discoveries made during my study and some of the findings are briefly mentioned as follows: (1) in relation to leukaemia disease we have discovered a new gene “TCF-1” that interacts with the “telomerase” gene. (2) With respect to yeast cell cycle analysis, we hypothesize that exoglucanase gene “exg1” is now implicated to be tied with “MCB cluster regulation” and a “mannosidase” with “histone linked mannoses”. A new quantitative prediction is that the time delay of the interaction between two genes seems to be approximately 30 minutes, or 0.17 cell cycles. Next, Cdc22, Suc22 and Mrc1 genes were discovered that interacts with each other as the potential candidates in controlling the Ribonucleotide reductase (RNR) activity. (3) Upon studying the phenomenon of Long Term Potentiation (LTP) it was found that the transcription factors, responsible for regulation of gene expression, begin to be elevated as soon as 30 min after induction of LTP, and remain elevated up to 2 hours. (4) Human microRNA data investigation resulted in the successful identification of two miRNA families i.e. let-7 and mir-30. (5) When we analysed the CNS cancer data, a set of 10 genes (HMG-I(Y), NBL1, UBPY, Dynein, APC, TARBP2, hPGT, LTC4S, NTRK3, and Gps2) was found to give 85% correct prediction on drug response. (6) Upon studying the AMPA, GABRA and NMDA receptors we hypothesize that phenylalanine (F at position 269) and leucine (L at position 353) in these receptors play the role of a binding centre for their interaction with several other genes/proteins such as c-jun, mGluR3, Jerky, BDNF, FGF-2, IGF-1, GALR1, NOS and S100beta. All the developed methods that we have used to discover above mentioned findings are very generic and can be easily applied on any dataset with some constraints. We believe that this research has established the significant fact that integrative use of various computational intelligence methods is critical to reveal new aspects of the problem and finally knowledge integration is also a must. During this coursework, I have significantly published this research in reputed international journals, presented results in several conferences and also produced book chapters.
11
King, James Lowell. "Gene Ontology-Guided Force-Directed Visualization of Protein Interaction Networks." Diss., NSUWorks, 2019. https://nsuworks.nova.edu/gscis_etd/1066.
Full textAbstract:
Protein interaction data is being generated at unprecedented rates thanks to advancements made in high throughput techniques such as mass spectrometry and DNA microarrays. Biomedical researchers, operating under budgetary constraints, have found it difficult to scale their efforts to keep up with the ever-increasing amount of available data. They often lack the resources and manpower required to analyze the data using existing methodologies. These research deficiencies impede our ability to understand diseases, delay the advancement of clinical therapeutics, and ultimately costs lives.
One of the most commonly used techniques to analyze protein interaction data is the construction and visualization of protein interaction networks. This research investigated the effectiveness and efficiency of novel domain-specific algorithms for visualizing protein interaction networks. The existing domain-agnostic algorithms were compared to the novel algorithms using several performance, aesthetic, and biological relevance metrics. The graph drawing algorithms proposed here introduced novel domain-specific forces to the existing force-directed graph drawing algorithms. The innovations include an attractive force and graph coarsening policy based on semantic similarity, and a novel graph refinement algorithm.
These experiments have demonstrated that the novel graph drawing algorithms consistently produce more biologically meaningful layouts than the existing methods. Aggregated over the 480 tests performed, and quantified using the Biological Evaluation Percentage metric defined in the Methodology chapter, the novel graph drawing algorithms created layouts that are 237 percent more biologically meaningful than the next best algorithm. This improvement came at the cost of additional edge crossings and smaller minimum angles between adjacent edges, both of which are undesirable aesthetics. The aesthetic and performance tradeoffs are experimentally quantified in this study, and dozens of algorithmically generated graph drawings are presented to visually illustrate the benefits of the novel algorithms. The graph drawing algorithms proposed in this study will help biomedical researchers to more efficiently produce high quality interactive protein interaction network drawings for improved discovery and communication.
12
Mungall, Christopher. "Next-generation information systems for genomics." Thesis, University of Edinburgh, 2011. http://hdl.handle.net/1842/5020.
Full textAbstract:
The advent of next-generation sequencing technologies is transforming biology by enabling individual researchers to sequence the genomes of individual organisms or cells on a massive scale. In order to realize the translational potential of this technology we will need advanced information systems to integrate and interpret this deluge of data. These systems must be capable of extracting the location and function of genes and biological features from genomic data, requiring the coordinated parallel execution of multiple bioinformatics analyses and intelligent synthesis of the results. The resulting databases must be structured to allow complex biological knowledge to be recorded in a computable way, which requires the development of logic-based knowledge structures called ontologies. To visualise and manipulate the results, new graphical interfaces and knowledge acquisition tools are required. Finally, to help understand complex disease processes, these information systems must be equipped with the capability to integrate and make inferences over multiple data sets derived from numerous sources. RESULTS: Here I describe research, design and implementation of some of the components of such a next-generation information system. I first describe the automated pipeline system used for the annotation of the Drosophila genome, and the application of this system in genomic research. This was succeeded by the development of a flexible graphoriented database system called Chado, which relies on the use of ontologies for structuring data and knowledge. I also describe research to develop, restructure and enhance a number of biological ontologies, adding a layer of logical semantics that increases the computability of these key knowledge sources. The resulting database and ontology collection can be accessed through a suite of tools. Finally I describe how the combination of genome analysis, ontology-based database representation and powerful tools can be combined in order to make inferences about genotype-phenotype relationships within and across species. CONCLUSION: The large volumes of complex data generated by high-throughput genomic and systems biology technology threatens to overwhelm us, unless we can devise better computing tools to assist us with its analysis. Ontologies are key technologies, but many existing ontologies are not interoperable or lack features that make them computable. Here I have shown how concerted ontology, tool and database development can be applied to make inferences of value to translational research.
13
Zhao, Meng. "DATA CAPTURE AND REPORT IN EPILEPSY MONITORING UNIT." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1380503113.
Full text
14
Macholan, Robert Daniel. "Analysis of Gene Expression Data for Gene Ontology Based Protein Function Prediction." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1301529255.
Full text
15
Spjuth, Ola. "Bioclipse integration of data and software in the life sciences /." Doctoral thesis, Uppsala : Acta Universitatis Upsaliensis, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-109305.
Full text
16
Hinderer, Eugene Waverly III. "COMPUTATIONAL TOOLS FOR THE DYNAMIC CATEGORIZATION AND AUGMENTED UTILIZATION OF THE GENE ONTOLOGY." UKnowledge, 2019. https://uknowledge.uky.edu/biochem_etds/43.
Full textAbstract:
Ontologies provide an organization of language, in the form of a network or graph, which is amenable to computational analysis while remaining human-readable. Although they are used in a variety of disciplines, ontologies in the biomedical field, such as Gene Ontology, are of interest for their role in organizing terminology used to describe—among other concepts—the functions, locations, and processes of genes and gene-products. Due to the consistency and level of automation that ontologies provide for such annotations, methods for finding enriched biological terminology from a set of differentially identified genes in a tissue or cell sample have been developed to aid in the elucidation of disease pathology and unknown biochemical pathways. However, despite their immense utility, biomedical ontologies have significant limitations and caveats. One major issue is that gene annotation enrichment analyses often result in many redundant, individually enriched ontological terms that are highly specific and weakly justified by statistical significance. These large sets of weakly enriched terms are difficult to interpret without manually sorting into appropriate functional or descriptive categories. Also, relationships that organize the terminology within these ontologies do not contain descriptions of semantic scoping or scaling among terms. Therefore, there exists some ambiguity, which complicates the automation of categorizing terms to improve interpretability.
We emphasize that existing methods enable the danger of producing incorrect mappings to categories as a result of these ambiguities, unless simplified and incomplete versions of these ontologies are used which omit problematic relations. Such ambiguities could have a significant impact on term categorization, as we have calculated upper boundary estimates of potential false categorizations as high as 121,579 for the misinterpretation of a single scoping relation, has_part, which accounts for approximately 18% of the total possible mappings between terms in the Gene Ontology. However, the omission of problematic relationships results in a significant loss of retrievable information. In the Gene Ontology, this accounts for a 6% reduction for the omission of a single relation. However, this percentage should increase drastically when considering all relations in an ontology. To address these issues, we have developed methods which categorize individual ontology terms into broad, biologically-related concepts to improve the interpretability and statistical significance of gene-annotation enrichment studies, meanwhile addressing the lack of semantic scoping and scaling descriptions among ontological relationships so that annotation enrichment analyses can be performed across a more complete representation of the ontological graph.
We show that, when compared to similar term categorization methods, our method produces categorizations that match hand-curated ones with similar or better accuracy, while not requiring the user to compile lists of individual ontology term IDs. Furthermore, our handling of problematic relations produces a more complete representation of ontological information from a scoping perspective, and we demonstrate instances where medically-relevant terms--and by extension putative gene targets--are identified in our annotation enrichment results that would be otherwise missed when using traditional methods. Additionally, we observed a marginal, yet consistent improvement of statistical power in enrichment results when our methods were used, compared to traditional enrichment analyses that utilize ontological ancestors. Finally, using scalable and reproducible data workflow pipelines, we have applied our methods to several genomic, transcriptomic, and proteomic collaborative projects.
17
Repchevskiy, Dmitry. "Ontology based data integration in life sciences." Doctoral thesis, Universitat de Barcelona, 2016. http://hdl.handle.net/10803/386411.
Full textAbstract:
The aim of this thesis is to develop standard and practical approaches for the semantic integration of biological data and services. The thesis considers various scenarios where ontologies may benefit bioinformatics web services development, integration and provenance. In spite of the broad use of ontologies in biology, their usage is usually limited to a definition of taxonomic hierarchies. This thesis examines the utility of ontologies for data integration in context of semantic web services development. The biological datatypes ontologies are very valuable for the data integration, especially in a context of continuous standards changes. The thesis evaluates the outdated BioMoby ontology for the generation of modern WS-I and RESTful web services. Another important aspect is the use of ontologies for the web services description. The thesis evaluates the W3C standard WSDL ontology for bioinformatics web services description and provenance. Finally, the integration with modern workflow execution platforms such as Taverna and Galaxy is also considered.
Despite the growing popularity of JSON format, web services vastly depend on XML type system. The OWL2XS tool facilitates semantic web services development providing the automatic XML Schema generation from an appropriate OWL 2 datatype ontology. Web services integration is hardly achievable without a broad standard adoption. The BioNemus application automatically generates standard-based web services from BioMoby ontologies. Semantic representation of web services description simplifies web services search and annotation. Semantic Web Services Registry (BioSWR) is based on W3C WSDL ontology and provides a multifaceted web services view in different formats: OWL 2, WSDL 1.1, WSDL 2.0 and WADL. To demonstrate benefits of ontology-based web services descriptions, BioSWR Taverna OSGI plug-in has been developed. The new, experimental, Taverna WSDL generic library has been used in Galaxy Gears tool which allows integrating web services into the Galaxy workflows.
The thesis explores the scopes of ontologies application for the biological data and services integration, providing a broad set of original tools.<br>El objetivo de la tesis es el desarrollo de una solución práctica y estándar para la integración semántica de los datos y servicios biológicos. La tesis estudia escenarios diferentes en los cuales las ontologías pueden beneficiar el desarrollo de los servicios web, su búsqueda y su visibilidad. A pesar de que las ontologías son ampliamente utilizadas en la biología, su uso habitualmente se limita a la definición de las jerarquías taxonómicas. La tesis examina la utilidad de las ontologías para la integración de los datos en el desarrollo de los servicios web semánticos. Las ontologías que definen los tipos de datos biológicos tienen un gran valor para la integración de los datos, especialmente ante un cambio continuo de los estándares. La tesis evalúa la ontología BioMoby para la generación de los servicios web conforme con las especificaciones WS-I y los servicios REST. Otro aspecto muy importante de la tesis es el uso de las ontologías para la descripción de los servicios web. La tesis evalúa la ontología WSDL promovida por el consorcio W3C para la descripción de los servicios y su búsqueda. Finalmente, se considera la integración con las plataformas modernas de la ejecución de los flujos de trabajo como Taverna y Galaxy.
A pesar de la creciente popularidad del formato JSON, los servicios web dependen mucho del XML. La herramienta OWL2XS facilita el desarrollo de los servicios web semánticos generando un esquema XML a partir de una ontología OWL 2. La integración de los servicios web es difícil de conseguir sin una adaptación de los estándares. La aplicación BioNemus genera de manera automática servicios web estándar a partir de las ontologías BioMoby. La representación semántica de los servicios web simplifica su búsqueda y anotación. El Registro Semántico de Servicios Web (BioSWR) está basado en la ontología WSDL del W3C y proporciona una representación en distintos formatos: OWL 2, WSDL 1.1, WSDL 2.0 y WADL. Para demostrar los beneficios de la descripción semántica de los servicios web se ha desarrollado un plugin para Taverna. También se ha implementado una nueva librería experimental que ha sido usada en la aplicación Galaxy Gears, la cual permite la integración de los servicios web en Galaxy.
La tesis explora el alcance de la aplicación de las ontologías para la integración de los datos y los servicios biológicos, proporcionando un amplio conjunto de nuevas aplicaciones.
18
Taniguti, Lucas Mitsuo. "Propagação semi-automática de termos Gene Ontology a proteínas com potencial biotecnológico para a produção de bioenergia." Universidade de São Paulo, 2014. http://www.teses.usp.br/teses/disponiveis/11/11137/tde-05012015-175313/.
Full textAbstract:
O aumento no volume de dados biológicos, oriundos principalmente do surgimento de sequenciadores de segunda geração, configura um desafio para a manutenção dos bancos de dados, que devem armazenar, disponibilizar e, no caso de bancos secundários, propagar informações biológicas para sequências sem caracterização experimental. Tal propagação é crucial , pois o fluxo com que novas sequências são depositadas é muito superior ao que proteínas são experimentalmente caracterizadas. De forma análoga ao EC number (Enzyme Commission number), a organização de proteínas em famílias visa organizar e facilitar operações automáticas nos bancos de dados. Dentro desse contexto este trabalho teve como objetivos a geração de modelos computacionais para famílias de proteínas envolvidas em processos microbianos biotecnologicamente interessantes para a produção de bioenergia. Para a geração dos modelos estatísticos foram escolhidas proteínas referência analisadas a priori em colaboração com o projeto MENGO1 . A partir da proteína referência foram realizadas buscas no UniProtKB com o objetivo de encontrar proteínas representativas para cada família e descrições de função com base na literatura científica. Com a coleção de sequências primárias das proteínas selecionadas foram realizados alinhamentos múltiplos de sequências com o programa MUSCLE 3.7 e posteriormente com o programa HMMER foram gerados os modelos computacionais (perfis de cadeia oculta de Markov). Os modelos passaram por consecutivas revisões para serem utilizados na propagação dos termos do Gene Ontology com confiança.Um total de 1.233 proteínas puderam receber os termos GO. Dessas proteínas 79% não apresentavam os termos GO disponibilizados no banco de dados UniProtKB. Uma comparação dos perfis-HMM com a utilização de redes de similaridade a um E-value de 10-14 confirmou a utilidade dos modelos na propagação adequada dos termos. Uma segunda validação utilizando um banco de dados construído com sequências aleatórias com base nos modelos e na frequência de codons das proteínas anotadas do SwisProt permitiu verificar a sensibilidade da estratégia quanto a recuperar membros não pertencentes aos modelos gerados.<br>The increase of biological data produced mainly by the second generation technologies stands as a challenge for the biological databases, that needs to adress issues like storage, data availability and, in the case of secondary databases, to propagate biological information to sequences with no experimental characterization. The propagation is important since the flow that new sequences are submited into databases is much higher than proteins having their function described by experiments. Similarly to the EC. number (Enzyme Commission number), an organization of protein families aims to organize and help automatic processes in databases. In this context this work had as goals the generation of computational models for protein families related to microbial processes with biotechnology potential for production of bioenergy. Several proteins annotated by MENGO2, a project in collaboration, were used as seeds to the statistic models. Alignments were made on UniProtKB, querying the seeds proteins, looking for representatives for each family generated and the existence of function descriptions referenced on the cientific literature. Multiple sequence alignment were made on each collection of seeds proteins, representatives of the families, thorough the MUSCLE 3.7 program, and after were generated the computational models (profile Hidden Markov Models) with the HMMER package. The models were consecutively reviewed until the curator consider it reliable for propagation of Gene Ontology terms. A set of 1,233 proteins from UniProtKB were classified in our families, suggesting that they could be annotated by the GO terms using MENGOfams families. From those proteins, 79% were not annotated by the MENGO specific GO terms. To compare the results that would be obtained using only BLAST similarity measures and using pHMMs we generated similarity networks, using an Evaue cutoff of 10-14. The results showed that the classification results of pHMMs are valuable for biological annotation propagation because it identifies precisely members of each family. A second analysis was applied for each family, using the respective pHMMs to query a collection of sequences generated by a null model. For null model were assumed that all sequences were not homologous and could be represented just by the aminoacid frequencies observed in the SwissProt database. No non-homologous proteins were classified as members by the MENGOfams models, suggesting that they were sensitive to identify only true member sequences.
19
Kronk, Clair Artemis. "Gender, Sex, and Sexual Orientation in Medicine: A Linguistic Analysis." University of Cincinnati / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1617107411106107.
Full text
20
Wimberley, James. "De novo Sequencing and Analysis of Salvia hispanica Transcriptome and Identification of Genes Involved in the Biosynthesis of Secondary Metabolites." Chapman University Digital Commons, 2019. https://digitalcommons.chapman.edu/cads_theses/5.
Full textAbstract:
Salvia hispanica L. (commonly known as chia) is gaining popularity worldwide and specially in US as a healthy oil and food supplement for human and animal consumption due to its favorable oil composition, and high protein, fiber, and antioxidant contents. Despite these benefits and its growing public demand, very limited gene sequence information is currently available in public databases. In this project, we generated 90 million high quality 150 bp paired-end sequences from the chia leaf and root tissues. The sequences were de novo assembled into 103,367 contigs with average length of 1,445 bp. The resulted assembly represented 92.2% transcriptome completeness. Around 69% of the assembled contigs were annotated against the uniprot database and represented a diverse array of functional and biological categories. A total of 14,267 contigs showed significant expression difference between the leaf and root tissues, with 6,151 and 8,116 contigs upregulated in the leaf and root, respectively. The sequence data generated in this project will provide valuable resources for future functional genomic research in chia. With the availability of transcriptome sequences, it would be possible to identify genes involved in the important metabolic pathways that give chia its unique nutritional and medicinal properties. Finally, the generated data will contribute to the genetic improvement efforts of chia to better serve the public demand.
21
Yu, Liyang. "An Indexation and Discovery Architecture for Semantic Web Services and its Application in Bioinformatics." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/cs_theses/20.
Full textAbstract:
Recently much research effort has been devoted to the discovery of relevant Web services. It is widely recognized that adding semantics to service description is the solution to this challenge. Web services with explicit semantic annotation are called Semantic Web Services (SWS). This research proposes an indexation and discovery architecture for SWS, together with a prototype application in the area of bioinformatics. In this approach, a SWS repository is created and maintained by crawling both ontology-oriented UDDI registries and Web sites that hosting SWS. For a given service request, the proposed system invokes the matching algorithm and a candidate set is returned with different degree of matching considered. This approach can add more flexibility to the current industry standards by offering more choices to both the service requesters and publishers. Also, the prototype developed in this research shows the value can be added by using SWS in application areas such as bioinformatics.
22
Griffith, Obi Lee. "Identification of gene expression changes in human cancer using bioinformatic approaches." Thesis, University of British Columbia, 2008. http://hdl.handle.net/2429/689.
Full textAbstract:
The human genome contains tens of thousands of gene loci which code for an even greater number of protein and RNA products. The highly complex temporal and spatial expression of these genes makes possible all the biological processes of life. Altered gene expression by mutation or deregulation is fundamental for the development of many human diseases. The ultimate aim of this thesis was to identify gene expression changes relevant to cancer. The advent of genome-wide expression profiling techniques, such as microarrays, has provided powerful new tools to identify such changes and researchers are now faced with an explosion of gene expression data. Processing, comparing and integrating these data present major challenges. I approached these challenges by developing and assessing novel methods for cross-platform analysis of expression data, scalable subspace clustering, and curation of experimental gene regulation data from the published literature. I found that combining results from different expression platforms increases reliability of coexpression predictions. However, I also observed that global correlation between platforms was generally low, and few gene pairs reached reasonable thresholds for high-confidence coexpression. Therefore, I developed a novel subspace clustering algorithm, able to identify coexpressed genes in experimental subsets of very large gene expression datasets. Biological assessment against several metrics indicates that this algorithm performs well. I also developed a novel meta-analysis method to identify consistently reported genes from differential expression studies when raw data are unavailable. This method was applied to thyroid cancer, producing a ranked list of significantly over-represented genes. Tissue microarray analysis of some of these candidates and others identified a number of promising biomarkers for diagnostic and prognostic classification of thyroid cancer. Finally, I present ORegAnno (www.oreganno.org), a resource for the community-driven curation of experimentally verified regulatory sequences. This resource has proven a great success with ~30,000 sequences entered from over 900 publications by ~50 contributing users. These data, methods and resources contribute to our overall understanding of gene regulation, gene expression, and the changes that occur in cancer. Such an understanding should help identify new cancer mechanisms, potential treatment targets, and have significant diagnostic and prognostic implications.
23
Blank, Carrine E., Hong Cui, Lisa R. Moore, and Ramona L. Walls. "MicrO: an ontology of phenotypic and metabolic characters, assays, and culture media found in prokaryotic taxonomic descriptions." BIOMED CENTRAL LTD, 2016. http://hdl.handle.net/10150/614758.
Full textAbstract:
Background: MicrO is an ontology of microbiological terms, including prokaryotic qualities and processes, material entities (such as cell components), chemical entities (such as microbiological culture media and medium ingredients), and assays. The ontology was built to support the ongoing development of a natural language processing algorithm, MicroPIE (or, Microbial Phenomics Information Extractor). During the MicroPIE design process, we realized there was a need for a prokaryotic ontology which would capture the evolutionary diversity of phenotypes and metabolic processes across the tree of life, capture the diversity of synonyms and information contained in the taxonomic literature, and relate microbiological entities and processes to terms in a large number of other ontologies, most particularly the Gene Ontology (GO), the Phenotypic Quality Ontology (PATO), and the Chemical Entities of Biological Interest (ChEBI). We thus constructed MicrO to be rich in logical axioms and synonyms gathered from the taxonomic literature. Results: MicrO currently has similar to 14550 classes (similar to 2550 of which are new, the remainder being microbiologically-relevant classes imported from other ontologies), connected by similar to 24,130 logical axioms (5,446 of which are new), and is available at (http://purl.obolibrary.org/obo/MicrO.owl) and on the project website at https://github.com/carrineblank/MicrO. MicrO has been integrated into the OBO Foundry Library (http://www.obofoundry.org/ontology/micro.html), so that other ontologies can borrow and re-use classes. Term requests and user feedback can be made using MicrO's Issue Tracker in GitHub. We designed MicrO such that it can support the ongoing and future development of algorithms that can leverage the controlled vocabulary and logical inference power provided by the ontology. Conclusions: By connecting microbial classes with large numbers of chemical entities, material entities, biological processes, molecular functions, and qualities using a dense array of logical axioms, we intend MicrO to be a powerful new tool to increase the computing power of bioinformatics tools such as the automated text mining of prokaryotic taxonomic descriptions using natural language processing. We also intend MicrO to support the development of new bioinformatics tools that aim to develop new connections between microbial phenotypes and genotypes (i.e., the gene content in genomes). Future ontology development will include incorporation of pathogenic phenotypes and prokaryotic habitats.
24
Chen, Eric Chun-Hung. "Fractionation Resistance of Duplicate Genes Following Whole Genome Duplication in Plants as a Function of Gene Ontology Category and Expression Level." Thesis, Université d'Ottawa / University of Ottawa, 2015. http://hdl.handle.net/10393/32789.
Full textAbstract:
With the proliferation of plant genomes being sequenced, assembled, and annotated, duplicate gene loss from whole genome duplication events, also known in plants as frac- tionation, has shown to have a different pattern from the classic gene duplication models described by Ohno in 1970. Models proposed more recently, the Gene Balance and Gene Dosage hypotheses, try to model this pattern. These models, however, disagree with each other on the relative importance of gene function and gene expression. In this thesis we explore the effects of gene function and gene expression on duplicate gene loss and retention.
We use gene sequence similarity and gene order conservation to construct our gene fam- ilies. We applied multiple whole genome comparison methods across various plants in rosids, asterids, and Poaceae in looking for a general pattern. We found that there is great consistency across different plant lineages. Genes categorized as metabolic genes with low level of expression have relatively low fractionation resistance, losing duplicate genes readily, while genes categorized as regulation and response genes with high level of expression have relatively high fractionation resistance, retaining more duplicate gene pairs or triples.
Though both gene function and gene expression have important effects on retention pattern, we found that gene function has a bigger effect than gene expression. Our results suggest that both the Gene Balance and Gene Dosage models account to some extent for fractionation resistance.
25
Yedida, Venkata Rama Kumar Swamy. "Protein Function Prediction Using Decision Tree Technique." University of Akron / OhioLINK, 2008. http://rave.ohiolink.edu/etdc/view?acc_num=akron1216313412.
Full text
26
Dutra, Marcio Branquinho. "Busca guiada de patentes de Bioinformática." Universidade de São Paulo, 2013. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-07022014-150130/.
Full textAbstract:
As patentes são licenças públicas temporárias outorgadas pelo Estado e que garantem aos inventores e concessionários a exploração econômica de suas invenções. Escritórios de marcas e patentes recomendam aos interessados na concessão que, antes do pedido formal de uma patente, efetuem buscas em diversas bases de dados utilizando sistemas clássicos de busca de patentes e outras ferramentas de busca específicas, com o objetivo de certificar que a criação a ser depositada ainda não foi publicada, seja na sua área de origem ou em outras áreas. Pesquisas demonstram que a utilização de informações de classificação nas buscas por patentes melhoram a eficiência dos resultados das consultas. A pesquisa associada ao trabalho aqui reportado tem como objetivo explorar artefatos linguísticos, técnicas de Recuperação de Informação e técnicas de Classificação Textual para guiar a busca por patentes de Bioinformática. O resultado dessa investigação é o Sistema de Busca Guiada de Patentes de Bioinformática (BPS), o qual utiliza um classificador automático para guiar as buscas por patentes de Bioinformática. A utilização do BPS é demonstrada em comparações com ferramentas de busca de patentes atuais para uma coleção específica de patentes de Bioinformática. No futuro, deve-se experimentar o BPS em coleções diferentes e mais robustas.<br>Patents are temporary public licenses granted by the State to ensure to inventors and assignees economical exploration rights. Trademark and patent offices recommend to perform wide searches in different databases using classic patent search systems and specific tools before a patent\'s application. The goal of these searches is to ensure the invention has not been published yet, either in its original field or in other fields. Researches have shown the use of classification information improves the efficiency on searches for patents. The objetive of the research related to this work is to explore linguistic artifacts, Information Retrieval techniques and Automatic Classification techniques, to guide searches for Bioinformatics patents. The result of this work is the Bioinformatics Patent Search System (BPS), that uses automatic classification to guide searches for Bioinformatics patents. The utility of BPS is illustrated by a comparison with other patent search tools. In the future, BPS system must be experimented with more robust collections.
27
Wang, Yuepeng. "Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI's brain gene ontology a thesis submitted to Auckland University of Technology in partial fulfilment of the requirements for the degree of Master of Computer and Information sciences, 2008 /." Click here to access this resource online, 2008. http://hdl.handle.net/10292/467.
Full text
28
Zaniboni, Gabriel Francisco. "Implementação de abordagens computacionais para identificação de RNAs longos não codificadores envolvidos na diferenciação neural." Universidade de São Paulo, 2015. http://www.teses.usp.br/teses/disponiveis/95/95131/tde-02022016-150323/.
Full textAbstract:
Cada vez mais, RNAs longos não codificadores (lncRNAs) surgem como importantes reguladores da biologia celular, principalmente em processos de diferenciação durante o desenvolvimento. O interesse no estudo das funções e mecanismos de atuação dessa classe de transcritos durante esses processos é crescente, e mostra-se bastante relevante no processo de diferenciação neural, pelo qual são gerados neurônios e células da glia. A linhagem celular P19, uma célula pluripotente advinda de um tipo de carcinoma embrionário murino, é bem consolidada como modelo in vitro de diferenciação neural. Após tratamento com ácido retinóico, ela é capaz de se diferenciar em neurônios e células da glia (astrócitos e oligodendrócitos). Em busca de evidências que indiquem a atuação de lncRNAs durante o processo de diferenciação neural, nosso grupo realizou experimentos utilizando microarranjos para averiguar os níveis de expressão gênica de lncRNAs e genes codificadores de proteínas (mRNAs) durante a diferenciação de células P19 em neurônios (predominância após 10 dias de diferenciação) e glia (predominância em 14 dias de diferenciação). Em um primeiro momento foi realizada a reanotação das sondas referentes a esses lncRNAs da plataforma de microarranjo, visto que as informações presentes nos arquivos de anotação da mesma eram muito escassas e desatualizadas. Registros de lncRNAs e mRNAs foram obtidos a partir de bancos de dados públicos para esse fim, e ao final dessa etapa aproximadamente 25,0% das sondas que não tinham uma anotação foram reanotadas com identificadores advindos desses bancos de dados. A partir dos dados de expressão, foram identificados todos os lncRNAs e mRNAs que apresentaram expressão diferencial entre as diferentes condições estudadas. As informações dos mRNAs diferencialmente expressos foram então utilizadas para a realização de análises de enriquecimento de categorias gênicas do Gene Ontology, nas ontologias de processo biológico e função molecular. A partir das sondas reanotadas, foram realizadas análises de coexpressão entre lncRNAs e mRNAs. A partir do cruzamento das informações obtidas, foram selecionados lncRNAs que através dos princípios de guilt by association se mostraram propensos a desempenharem um papel regulatório na diferenciação neural. Assim, as informações geradas nesse trabalho servirão como base para estudos futuros de validação funcional desses lncRNAs.<br>Increasingly, long noncoding RNAs (lncRNAs) emerge as important regulators of cell biology, especially in differentiation processes during development. The interest in the study of functions and mechanisms of action of this class of transcripts during these processes is growing, and shows quite relevant in the neural differentiation process by which neurons and glia are generated. The P19 cell line, pluripotent cells arising from a type of murine embryonal carcinoma, is well established as an in vitro model of neural differentiation. After treatment with retinoic acid, it is capable of differentiating into neurons and glial cells (astrocytes and oligodendrocytes). In search of evidence that indicate the action of lncRNAs during the neural differentiation process, our group conducted experiments using microarrays to assess gene expression levels of lncRNAs and protein coding genes (mRNAs) during differentiation of P19 cells into neurons (mainly after 10 days of differentiation) and glial cells (mainly after 14 days of differentiation). At first was performed the reannotation of the probes relating to these microarrays lncRNAs, as the information provided in the annotation files were very scarce or outdated. LncRNAs and mRNAs records were obtained from public databases for this purpose, and at the end of this stage approximately 25.0% of the probes without annotation were reannotated with identifiers arising from these databases. From the expression data, we identified all lncRNAs and mRNAs that showed differential expression between the different studied conditions. The information of differentially expressed mRNAs were then used to perform Gene Ontology enrichment, in the ontologies biological process and molecular function. From the reannotated probes, coexpression analyses were performed for lncRNAs and mRNAs. From the crosscheck of information obtained, we selected those lncRNAs that by the principles of guilt by association proved likely to play a regulatory role in neural differentiation. Thus, the information generated in this study will serve as a basis for future studies of functional validation of these lncRNAs.
29
GUDIVADA, RANGA CHANDRA. "DISCOVERY AND PRIORITIZATION OF BIOLOGICAL ENTITIES UNDERLYING COMPLEX DISORDERS BY PHENOME-GENOME NETWORK INTEGRATION." University of Cincinnati / OhioLINK, 2007. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1195161740.
Full text
30
Feroce, Marcello. "Cros-Organism Annotation Prediction through Deep Learning Algorithms." Bachelor's thesis, Alma Mater Studiorum - Università di Bologna, 2017. http://amslaurea.unibo.it/14244/.
Full textAbstract:
Studying how genes or proteins influence humans and other species' lives is paramount. To study that, it's necessary to know which functional properties are specific for each gene or protein. The association between one gene or protein and a functional properties is called annotation. An annotion can be 0 or 1. 1 means that gene or protein contributes to the activation of a certain functional property. Functional properties are referred by terms, which are strings that belong to ontologies. This work aim is to predict novel gene annotations for little know species such as Bos Taurus. To predict such annotations, a model, built using deep learning, is used.
This model is trained using well know species as Mus Musculus or Homo Sapiens. Every predicted annotation has its own likelihood, that tells about how much the prediction is close to a 0 or a 1. Final accuracy can be evaluated fixing a certain value of likelihood, so that all the considered annotations have a likelihood greater or equal than the fixed one. The obtained accuracy is quite high but not enought to be used in a professional way, although it offers a nice cue for future research.
31
Jayapandian, Catherine Praveena. "Cloudwave: A Cloud Computing Framework for Multimodal Electrophysiological Big Data." Case Western Reserve University School of Graduate Studies / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=case1405516626.
Full text
32
Hassan, Aamir Ul. "Integration of Genome Scale Data for Identifying New Biomarkers in Colon Cancer: Integrated Analysis of Transcriptomics and Epigenomics Data from High Throughput Technologies in Order to Identifying New Biomarkers Genes for Personalised Targeted Therapies for Patients Suffering from Colon Cancer." Thesis, University of Bradford, 2017. http://hdl.handle.net/10454/17419.
Full textAbstract:
Colorectal cancer is the third most common cancer and the leading cause of cancer deaths in Western industrialised countries. Despite recent advances in the screening, diagnosis, and treatment of colorectal cancer, an estimated 608,000 people die every year due to colon cancer. Our current knowledge of colorectal carcinogenesis indicates a multifactorial and multi-step process that involves various genetic alterations and several biological pathways. The identification of molecular markers with early diagnostic and precise clinical outcome in colon cancer is a challenging task because of tumour heterogeneity.
This Ph.D.-thesis presents the molecular and cellular mechanisms leading to colorectal cancer. A systematical review of the literature is conducted on Microarray Gene expression profiling, gene ontology enrichment analysis, microRNA and system Biology and various bioinformatics tools.
We aimed this study to stratify a colon tumour into molecular distinct subtypes, identification of novel diagnostic targets and prediction of reliable prognostic signatures for clinical practice using microarray expression datasets. We performed an integrated analysis of gene expression data based on genetic, epigenetic and extensive clinical information using unsupervised learning, correlation and functional network analysis. As results, we identified 267-gene and 124-gene signatures that can distinguish normal, primary and metastatic tissues, and also involved in important regulatory functions such as immune-response, lipid metabolism and peroxisome proliferator-activated receptors (PPARs) signalling pathways.
For the first time, we also identify miRNAs that can differentiate between primary colon from metastatic and a prognostic signature of grade and stage levels, which can be a major contributor to complex transcriptional phenotypes in a colon tumour.
33
Bettembourg, Charles. "Méthodes sémantiques pour la comparaison inter-espèces de voies métaboliques : application au métabolisme des lipides chez l'humain, la souris et la poule." Phd thesis, Université Rennes 1, 2013. http://tel.archives-ouvertes.fr/tel-00926498.
Full text
34
Cakmak, Ali. "Mining Metabolic Networks and Biomedical Literature." Case Western Reserve University School of Graduate Studies / OhioLINK, 2009. http://rave.ohiolink.edu/etdc/view?acc_num=case1223490345.
Full text
35
Miñarro, Giménez José Antonio. "Entorno para la Gestión Semántica de Información Biomédica en Investigación Traslacional." Doctoral thesis, Universidad de Murcia, 2012. http://hdl.handle.net/10803/92299.
Full textAbstract:
Las investigaciones traslacionales tienen el objetivo de poner a disposición de investigaciones las evidencias obtenidas en investigaciones básicas para ensayos clínicos. Para facilitar la investigación traslacional es necesario relacionar dicha información mediante la integración de repositorios de información biológica y médica.
Debido a la complejidad, cantidad, diversidad y rápida evolución de la información biológica, es imposible gestionar los repositorios biológicos de manera manual ya que supondría una gran inversión en tiempo y en esfuerzo. Por lo tanto, cada vez más es necesario dotar de nuevas herramientas de gestión que faciliten esta tarea y pueda ser realizada de manera autónoma.
Esta tesis presenta un entorno para la gestión e integración semántica utilizado las tecnologías de la Web semántica, las cuales son utilizadas para representar, almacenar, explotar y guiar el proceso de integración de la información y conocimiento. Como resultado principal se integraron repositorios de genes y proteínas ortólogas con enfermedades genéticas.<br>Translational research aims to connect basic biomedical researches with clinical research in order to reach new conclusions based on biomedical evidences. To facilitate the translational research, biological and biomedical information must be related. So, we need to integrate biological and biomedical repositories.
Life sciences is a knowledge based discipline, in the data and knowledge is represented through vast amounts of complex and changing information stored in disparate resources and in machine-unfriendly formats. Therefore, the availability of computational methods for organizing, accessing and retrieving information in a systematic way has become crucial for the progress of research in life sciences.
In this thesis, we present a framework for the semantic management and integration using semantic web technologies. This framework assists life scientists in the exploration of orthologs/genetic diseases research paths by providing a precise, explicit meaning for information units and intertwining such information.
36
Magka, Despoina. "Foundations and applications of knowledge representation for structured entities." Thesis, University of Oxford, 2013. http://ora.ox.ac.uk/objects/uuid:4a3078cc-5770-4a9b-81d4-8bc52b41e294.
Full textAbstract:
Description Logics form a family of powerful ontology languages widely used by academics and industry experts to capture and intelligently manage knowledge about the world. A key advantage of Description Logics is their amenability to automated reasoning that enables the deduction of knowledge that has not been explicitly stated. However, in order to ensure decidability of automated reasoning algorithms, suitable restrictions are usually enforced on the shape of structures that are expressible using Description Logics. As a consequence, Description Logics fall short of expressive power when it comes to representing cyclic structures, which abound in life sciences and other disciplines. The objective of this thesis is to explore ontology languages that are better suited for the representation of structured objects. It is suggested that an alternative approach which relies on nonmonotonic existential rules can provide a promising candidate for modelling such domains. To this end, we have built a comprehensive theoretical and practical framework for the representation of structured entities along with a surface syntax designed to allow the creation of ontological descriptions in an intuitive way. Our formalism is based on nonmonotonic existential rules and exhibits a favourable balance between expressive power and computational as well as empirical tractability. In order to ensure decidability of reasoning, we introduce a number of acyclicity criteria that strictly generalise many of the existing ones. We also present a novel stratification condition that properly extends `classical' stratification and allows for capturing both definitional and conditional aspects of complex structures. The applicability of our formalism is supported by a prototypical implementation, which is based on an off-the-shelf answer set solver and is tested over a realistic knowledge base. Our experimental results demonstrate improvement of up to three orders of magnitude in comparison with previous evaluation efforts and also expose numerous modelling errors of a manually curated biochemical knowledge base. Overall, we believe that our work lays the practical and theoretical foundations of an ontology language that is well-suited for the representation of structured objects. From a modelling point of view, our approach could stimulate the adoption of a different and expressive reasoning paradigm for which robustly engineered mature reasoners are available; it could thus pave the way for the representation of a broader spectrum of knowledge. At the same time, our theoretical contributions reveal useful insights into logic-based knowledge representation and reasoning. Therefore, our results should be of value to ontology engineers and knowledge representation researchers alike.
37
Kumar, Vivek. "Computational Prediction of Protein-Protein Interactions on the Proteomic Scale Using Bayesian Ensemble of Multiple Feature Databases." University of Akron / OhioLINK, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=akron1322489637.
Full text
38
Wheeler, Gregory Lawrence. "Plant Carnivory and the Evolution of Novelty in Sarracenia alata." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1531948732481904.
Full text
39
Sutharzan, Sreeskandarajan. "CLUSTERING AND VISUALIZATION OF GENOMIC DATA." Miami University / OhioLINK, 2019. http://rave.ohiolink.edu/etdc/view?acc_num=miami1563973517163859.
Full text
40
Cabral, Heleno Carmo Borges. "A DEFINIÇÃO DE UMA ONTOLOGIA PARA INTEGRAR DADOS DE INTERATOMA E TRANSCRIPTOMA DE CÂNCER." Universidade Franciscana, 2010. http://tede.universidadefranciscana.edu.br:8080/handle/UFN-BDTD/247.
Full textAbstract:
Made available in DSpace on 2018-06-27T18:56:06Z (GMT). No. of bitstreams: 3
Heleno Carmo Borges Cabral.pdf: 5857862 bytes, checksum: d2951b87950c843d49760204923bdc2a (MD5)
Heleno Carmo Borges Cabral.pdf.txt: 118091 bytes, checksum: e4e222642f43d380a5e6bf3a2acd2aac (MD5)
Heleno Carmo Borges Cabral.pdf.jpg: 3434 bytes, checksum: 3c397ca213b4291d6533cce010f30ed4 (MD5)
Previous issue date: 2010-06-23<br>Coordenação de Aperfeiçoamento de Pessoal de Nível Superior<br>Ontocancro is an ontology stored in a knowledge database designed to be a source of
information to integrate transcriptomics and interatomics data involved in gene pathways of
genome maintenance/stability mechanisms (GMM). Genome maintenance mechanisms are
shown to be critical for cell homeostasis since their malfunctioning can predispose to cancer.
Repair, apoptosis and chromosome stability pathways comprise the cornerstone of GMM. The
information about these pathways are disseminated in various databases as NCI-Nature,
BioCarta, KEGG, Reactome, Prosite, GO and others. Ontocancro was created with the
intention of integratin the information of genes involved in GMM from several curated
databases. This data integration is difficult for biological data lack a unified vocabulary and
need constant update what is provided by Ontocancro. Additionally, it allows the integration
of transcriptome data provided by some Affymetrix microarrays platforms with interactome
data from the STRING database, which has information about protein interactions. So, this
work shows the integration of data from biological information systems using the ontology
paradigm, in order to integrate transcriptomics and interatomics data involved in gene
pathways of genome stability.<br>A Ontocancro é uma ontologia armazenada em um banco de dados de conhecimento projetada
para ser a fonte de informação referente a integração de dados de interatoma e transcriptoma
envolvidos em vias metabólicas de mecanismo de manutenção do genoma humano (GMM).
Esse mecanismo de manutenção são críticos para homeostase celular desde o seu mau
funcionamento, o que pode causar câncer. O reparo, a apoptose e as vias de estabilidade
cromossômicas compreendem o cerne do GMM. A informação sobre essas vias metabólicas
são disseminadas em vários bancos de dados, como o NCI-Nature, o BioCarta, o KEGG, o
Reactome, o Prosite e o GO, entre outros. A ontologia Ontocancro foi criada com a intenção
de integrar a informação sobre os genes envolvidos em GMM a partir de diversos bancos de
dados curados. Essa integração de dados é complexa pela falta de um vocabulário sobre os
dados biológicos e a necessidade constante de atualização destes dados. Para sanar essas duas
dificuldades, a Ontocancro foi criada. Adicionalmente, ela permite a integração de dados
oriundos de transcriptoma obtidos a partir da plataforma Affymetrix com os dados de
interatoma obtidos a partir do banco de dados chamado STRING, o qual possui informação
sobre as interações entre as proteínas. Portanto, este trabalho apresenta a integração de dados
obtidos de sistemas de informação biológicos usando o paradigma ontológico, de forma a
integrar os dados envolvidos em interatoma e transcriptoma em vias metabólicas de
estabilidade do genoma.
41
Broderick, Shaun Robert. "Pollination-Induced Gene Changes That Lead to Senescence in Petunia × hybrida." The Ohio State University, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=osu1408958432.
Full text
42
Chniti, Amina. "Gestion des dépendances et des interactions entre Ontologies et Règles Métier." Phd thesis, Université Pierre et Marie Curie - Paris VI, 2013. http://tel.archives-ouvertes.fr/tel-00820671.
Full textAbstract:
Vu la rapidité de l'évolution des connaissances des domaines, la maintenance des systèmes d'information est devenue de plus en plus difficile à gérer. Afin d'assurer une flexibilité de ces systèmes, nous proposons une approche qui permet de représenter les connaissances des domaines dans des modèles de représentation des connaissances plutôt que de les coder, dans un langage de programmation informatique, dans l'application du domaine. Ceci assurerait une meilleure flexibilité des systèmes d'information, faciliterait leur maintenance et permettrait aux experts métier de gérer eux même l'évolution des connaissances de leur domaine. Pour cela, nous proposons une approche qui permet d'intégrer des ontolo- gies et des règles métier. Les ontologies permettent de modéliser les connais- sances d'un domaine. Les règles permettent aux experts métier de définir et d'automatiser, dans un langage naturel contrôlé, des décisions du métier en se fondant sur les connaissances représentées dans l'ontologie. Ainsi, les règles dépendent des entités modélisées dans l'ontologie. Vu cette dépendance, il est nécessaire d'étudier l'impact de l'évolution des ontologies sur les règles. Pour cela, nous proposons l'approche MDR (Modéliser - Détecter - Réparer) qui permet de modéliser des changements d'ontologies, de détecter les problèmes de cohérence qu'ils peuvent causer sur les règles métier et de proposer des solutions pour réparer ces problèmes. L'approche proposée est une approche orientée experts métier et est fondée sur les systèmes de gestion des règles métier.
43
Benabderrahmane, Sidahmed. "Prise en compte des connaissances du domaine dans l'analyse transcriptomique : Similarité sémantique, classification fonctionnelle et profils flous : application au cancer colorectal." Phd thesis, Université Henri Poincaré - Nancy I, 2011. http://tel.archives-ouvertes.fr/tel-00653169.
Full textAbstract:
L'analyse bioinformatique des données de transcriptomique a pour but d'identifier les gènes qui présentent des variations d'expression entre différentes situations, par exemple entre des échantillons de tissu sain et de tissu malade et de caractériser ces gènes à partir de leurs annotations fonctionnelles. Dans ce travail de thèse, je propose quatre contributions pour la prise en compte des connaissances du domaine dans ces méthodes. Tout d'abord je définis une nouvelle mesure de similarité sémantique et fonctionnelle (IntelliGO) entre les gènes, qui exploite au mieux les annotations fonctionnelles issues de l'ontologie GO ('Gene Ontology'). Je montre ensuite, grâce à une méthodologie d'évaluation rigoureuse, que la mesure IntelliGO est performante pour la classification fonctionnelle des gènes. En troisième contribution je propose une approche différentielle avec affectation floue pour la construction de profils d'expression différentielle (PED). Je définis alors un algorithme d'analyse de recouvrement entre classes fonctionnelles et ensemble des références, ici les PEDs, pour mettre en évidence des gènes ayant à la fois les mêmes variations d'expression et des annotations fonctionnelles similaires. Cette méthode est appliquée à des données expérimentales produites à partir d'échantillons de tissus sains, de tumeur colo-rectale et de lignée cellulaire cancéreuse. Finalement, la mesure de similarité IntelliGO est généralisée à d'autres vocabulaires structurés en graphe acyclique dirigé et enraciné (rDAG) comme l'est l'ontologie GO, avec un exemple d'application concernant la réduction sémantique d'attributs avant la fouille.
44
劉許吉. "Ontology Driven Bioinformatics Data Exploration." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/74987712409816276865.
Full textAbstract:
碩士<br>國立交通大學<br>資訊科學系所<br>92<br>This thesis proposes an integrated bioinformatics environment, called ODEX, that allows flexible integration of potentially distributed, heterogeneous bioinformatics tools at different levels, ranging from development-time, component-based integration, to dynamic, rule-based tool composition, to ontology-assisted data exploration linking tools with domain-specific knowledge. ODEX supports users with different expertise and skills, and helps them manage different analysis tools and database systems. Furthermore, with the help of ontology, ODEX can suggest suitable analysis steps, guiding user in the exploration of highly complex, interconnected biological data.
45
Dippold, Mindi M. "A Biological and Bioinformatics Ontology for Service Discovery and Data Integration." Thesis, 2006. http://hdl.handle.net/1805/621.
Full textAbstract:
Submitted to the faculty of Indiana University in partial fulfillment of the requirements for the degree Masters of Science in the School of Informatics Indiana University December 2005<br>This project addresses the need for an increased expressivity and robustness of ontologies already supporting BACIIS and SIBIOS, two systems for data and service integration in the life sciences. The previous ontology solutions as global schema and facilitator of service discovery sustained the purposes for which they were built to provide, but were in need of updating in order to keep up with more recent standards in ontology descriptions and utilization as well as increase the breadth of the domain and expressivity of the content. Thus, several tasks were undertaken to increase the worth of the system ontologies. These include an upgrade to a more recent ontology language standard, increased domain coverage, and increased expressivity via additions of relationships and hierarchies within the ontology as well as increased ease of maintenance by a distributed design.
46
Schlicker, Andreas [Verfasser]. "Ontology-based similarity measures and their application in bioinformatics / eingereicht von Andreas Schlicker." 2010. http://d-nb.info/1008430242/34.
Full text
47
Smaili, Fatima Z. "Machine Learning Models for Biomedical Ontology Integration and Analysis." Diss., 2020. http://hdl.handle.net/10754/665189.
Full textAbstract:
Biological knowledge is widely represented in the form of ontologies and ontology-based annotations. Biomedical ontologies describe known phenomena in biology using formal axioms, and the annotations associate an entity (e.g. genes, diseases, chemicals, etc.) with a set of biological concepts. In addition to formally structured axioms, ontologies contain meta-data in the form of annotation properties expressed mostly in natural language which provide valuable pieces of information that characterize ontology concepts. The structure and information contained in ontologies and their annotations make them valuable for use in machine learning, data analysis and knowledge extraction tasks.
I develop the first approaches that can exploit all of the information encoded in ontologies, both formal and informal, to learn feature embeddings of biological concepts and biological entities based on their annotations to ontologies. Notably, I develop the first approach to use all the formal content of ontologies in the form of logical axioms and entity annotations to generate feature vectors of biological entities using neural language models. I extend the proposed algorithm by enriching the obtained feature vectors through representing the natural language annotation properties within the ontology meta-data as axioms. Transfer learning is then applied to learn from the biomedical literature and apply on the formal knowledge of ontologies.
To optimize learning that combines the formal content of biomedical ontologies and natural language data such as the literature, I also propose a new approach that uses self-normalization with a deep Siamese neural network that improves learning from both the formal knowledge within ontologies and textual data.
I validate the proposed algorithms by applying them to the Gene Ontology to generate feature vectors of proteins based on their functions, and to the PhenomeNet ontology to generate features of genes and diseases based on the phenotypes they are associated with. The generated features are then used to train a variety of machinelearning based classifiers to perform different prediction tasks including the prediction of protein interactions, gene–disease associations and the toxicological effects of chemicals. I also use the proposed methods to conduct the first quantitative evaluation of the quality of the axioms and meta-data included in ontologies to prove that including axioms as background improves ontology-based prediction.
The proposed approaches can be applied to a wide range of other bioinformatics research problems including similarity-based prediction and classification of interaction types using supervised learning, or clustering.
48
Mainz, Indra [Verfasser]. "Development and implementation of techniques for ontology engineering and an ontology-based search for bioinformatics tools and methods / vorgelegt von Indra Mainz." 2008. http://d-nb.info/99269776X/34.
Full text
49
Wang, Yuepeng. "Integrative methods for gene data analysis and knowledge discovery on the case study of KEDRI’s brain gene ontology." 2008. http://hdl.handle.net/10292/467.
Full textAbstract:
In 2003, Pomeroy et al. published a research study that described a gene expression based prediction of central nervous system embryonal tumour (CNS) outcome. Over a half of decade, many models and approaches have been developed based on experimental data consisting of 99 samples with 7,129 genes. The way, how meaningful knowledge from these models can be extracted, and how this knowledge for further research is still a hot topic. This thesis addresses this and has developed an information method that includes modelling of interactive patterns, important genes discovery and visualisation of the obtained knowledge. The major goal of this thesis is to discover important genes responsible for CNS tumour and import these genes into a well structured knowledge framework system, called Brain-Gene-Ontology. In this thesis, we take the first step towards finding the most accurate model for analysing the CNS tumour by offering a comparative study of global, local and personalised modelling. Five traditional modelling approaches and a new personalised method – WWKNN (weighted distance, weighted variables K-nearest neighbours) – are investigated. To increase the classification accuracy and one-vs.-all based signal to- noise ratio is also developed for pre-processing experimental data. For the knowledge discovery, CNS-based ontology system is developed. Through ontology analysis, 21 discriminate genes are found to be relevant for different CNS tumour classes, medulloblastoma tumour subclass and medulloblastoma treatment outcome. All the findings in this thesis contribute for expanding the information space of the BGO framework.
50
Dönitz, Jürgen. "Development and application of ontologies for biological applications." Thesis, 2016. http://hdl.handle.net/11858/00-1735-0000-0028-86B9-B.
Full text