Relevant bibliographies by topics / Bioinformatics Ontology

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers

Academic literature on the topic 'Bioinformatics Ontology'

Author: Grafiati
Published: 4 June 2021
Last updated: 16 February 2022

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Journal articles on the topic "Bioinformatics Ontology"

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Giudicelli, V., and M. P. Lefranc. "Ontology for immunogenetics: the IMGT-ONTOLOGY." Bioinformatics 15, no. 12 (1999): 1047–54. http://dx.doi.org/10.1093/bioinformatics/15.12.1047.

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Moore, B., G. Fan, and K. Eilbeck. "SOBA: sequence ontology bioinformatics analysis." Nucleic Acids Research 38, Web Server (2010): W161—W164. http://dx.doi.org/10.1093/nar/gkq426.

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Baker, P. G., C. A. Goble, S. Bechhofer, N. W. Paton, R. Stevens, and A. Brass. "An ontology for bioinformatics applications." Bioinformatics 15, no. 6 (1999): 510–20. http://dx.doi.org/10.1093/bioinformatics/15.6.510.

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Stevens, R. "Ontology-based knowledge representation for bioinformatics." Briefings in Bioinformatics 1, no. 4 (2000): 398–414. http://dx.doi.org/10.1093/bib/1.4.398.

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Stevens, Robert. "Ontology Based Document Enrichment in Bioinformatics." Comparative and Functional Genomics 3, no. 1 (2002): 42–46. http://dx.doi.org/10.1002/cfg.141.

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Controlled vocabularies are common within bioinformatics resources. They can be used to give a summary of the knowledge held about a particular entity. They are also used to constrain values given for particular attributes of an entity. This helps create a shared understanding of a domain and aids increased precision and recall during querying of resources. Ontologies can also provide such facilities, but can also enhance their utility. Controlled vocabularies are often simply lists of words, but may be viewed as a kind of ontology. Ideally ontologies are structurally enriched with relationships between terms within the vocabulary. Use of such rich forms of vocabularies in database annotation could enhance those resources usability by both humans and computers. The representation of the knowledge content of biological resources in a computationally accessible form opens the prospect of greater support for a biologist investigating new data.
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Viti, Federica, Ivan Merelli, Andrea Calabria, et al. "Ontology-based resources for bioinformatics analysis." International Journal of Metadata, Semantics and Ontologies 6, no. 1 (2011): 35. http://dx.doi.org/10.1504/ijmso.2011.042488.

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Stevens, R., C. Goble, I. Horrocks, and S. Bechhofer. "Building a bioinformatics ontology using OIL." IEEE Transactions on Information Technology in Biomedicine 6, no. 2 (2002): 135–41. http://dx.doi.org/10.1109/titb.2002.1006301.

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Du, P., G. Feng, J. Flatow, et al. "From disease ontology to disease-ontology lite: statistical methods to adapt a general-purpose ontology for the test of gene-ontology associations." Bioinformatics 25, no. 12 (2009): i63—i68. http://dx.doi.org/10.1093/bioinformatics/btp193.

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Shuang Qiu, Yadong Wang, Liran Juan, Mingxiang Teng, and Liang Cheng. "Bioinformatics Database Integration Based on Biomedical Ontology." International Journal of Advancements in Computing Technology 3, no. 2 (2011): 66–75. http://dx.doi.org/10.4156/ijact.vol3.issue2.9.

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Digiampietri, Luciano A., Jose de J. Perez Alcazar, and Claudia Bauzer Medeiros. "An ontology-based framework for bioinformatics workflows." International Journal of Bioinformatics Research and Applications 3, no. 3 (2007): 268. http://dx.doi.org/10.1504/ijbra.2007.015003.

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Dissertations / Theses on the topic "Bioinformatics Ontology"

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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.

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<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>
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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.

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<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>
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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.

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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.

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<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>
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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.

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<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>
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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.

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<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>
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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.

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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.

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<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>
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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.

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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.
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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.

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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.
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Books on the topic "Bioinformatics Ontology"

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Applied ontology: An introduction. Ontos Verlag, 2008.

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Tianhua, Niu, ed. Ontologies for bioinformatics. MIT Press, 2005.

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Baclawski, Kenneth, Michael S. Waterman, Pavel Pevzner, and Tianhua Niu. Ontologies for Bioinformatics. MIT Press, 2005.

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Book chapters on the topic "Bioinformatics Ontology"

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Harris, Midori A. "Developing an Ontology." In Bioinformatics. Humana Press, 2008. http://dx.doi.org/10.1007/978-1-60327-159-2_5.

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Arighi, Cecilia N., Harold Drabkin, Karen R. Christie, Karen E. Ross, and Darren A. Natale. "Tutorial on Protein Ontology Resources." In Protein Bioinformatics. Springer New York, 2017. http://dx.doi.org/10.1007/978-1-4939-6783-4_3.

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Dimmer, Emily, Tanya Z. Berardini, Daniel Barrell, and Evelyn Camon. "Methods for Gene Ontology Annotation." In Plant Bioinformatics. Humana Press, 2007. http://dx.doi.org/10.1007/978-1-59745-535-0_24.

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Schofield, Paul N., Björn Rozell, and Georgios V. Gkoutos. "Towards a Disease Ontology." In Anatomy Ontologies for Bioinformatics. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-885-2_5.

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Lambrix, Patrick, and He Tan. "Ontology Alignment and Merging." In Anatomy Ontologies for Bioinformatics. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-885-2_6.

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Cooper, Laurel, and Pankaj Jaiswal. "The Plant Ontology: A Tool for Plant Genomics." In Plant Bioinformatics. Springer New York, 2016. http://dx.doi.org/10.1007/978-1-4939-3167-5_5.

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Khodiyar, Varsha K., Emily C. Dimmer, Rachael P. Huntley, and Ruth C. Lovering. "Fundamentals of Gene Ontology Functional Annotation." In Knowledge-Based Bioinformatics. John Wiley & Sons, Ltd, 2010. http://dx.doi.org/10.1002/9780470669716.ch8.

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Greenbaum, Jason A., Randi Vita, Laura M. Zarebski, Alessandro Sette, and Bjoern Peters. "Ontology Development for the Immune Epitope Database." In Bioinformatics for Immunomics. Springer New York, 2009. http://dx.doi.org/10.1007/978-1-4419-0540-6_4.

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Zhou, Tao, Jun Yao, and Zhanjiang Liu. "Gene Ontology, Enrichment Analysis, and Pathway Analysis." In Bioinformatics in Aquaculture. John Wiley & Sons, Ltd, 2017. http://dx.doi.org/10.1002/9781118782392.ch10.

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Haendel, Melissa A., Fabian Neuhaus, David Osumi-Sutherland, et al. "CARO – The Common Anatomy Reference Ontology." In Anatomy Ontologies for Bioinformatics. Springer London, 2008. http://dx.doi.org/10.1007/978-1-84628-885-2_16.

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Conference papers on the topic "Bioinformatics Ontology"

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Shah, Abad Ali, Fatima Maqbool, and Muhammad Usman Ghani Khan. "Dynamic ontology management for bioinformatics domain." In 2013 International Conference on Open Source Systems and Technologies (ICOSST). IEEE, 2013. http://dx.doi.org/10.1109/icosst.2013.6720596.

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Menager, Herve, Zoe Lacroix, and Pierre Tuffery. "Bioinformatics Services Discovery Using Ontology Classification." In 2007 IEEE Congress on Services (Services 2007). IEEE, 2007. http://dx.doi.org/10.1109/services.2007.20.

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Dessi, Nicoletta, Giuliano Ferrentino, Emanuele Pascariello, and Barbara Pes. "Towards Ontology-Enabled BioContexts for Bioinformatics Research." In 2015 IEEE 24th International Conference on Enabling Technologies: Infrastructure for Collaborative Enterprises (WETICE). IEEE, 2015. http://dx.doi.org/10.1109/wetice.2015.17.

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LAMBRIX, P., and A. EDBERG. "EVALUATION OF ONTOLOGY MERGING TOOLS IN BIOINFORMATICS." In Proceedings of the Pacific Symposium. WORLD SCIENTIFIC, 2002. http://dx.doi.org/10.1142/9789812776303_0055.

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Alterovitz, Gil, Michael Xiang, and Marco F. Ramoni. "An Information Theoretic Framework for Ontology-based Bioinformatics." In 2007 Information Theory and Applications Workshop. IEEE, 2007. http://dx.doi.org/10.1109/ita.2007.4357555.

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Cannataro, M., P. H. Guzzi, T. Mazza, G. Tradigo, and P. Veltri. "Algorithms and databases in bioinformatics: towards a proteomic ontology." In International Conference on Information Technology: Coding and Computing (ITCC'05) - Volume II. IEEE, 2005. http://dx.doi.org/10.1109/itcc.2005.63.

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Navathe, Professor Shamkant B. "Text Mining and Ontology Applications in Bioinformatics and GIS." In Sixth International Conference on Machine Learning and Applications (ICMLA 2007). IEEE, 2007. http://dx.doi.org/10.1109/icmla.2007.122.

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Huang, Minlie, Xiaoyan Zhu, Shilin Ding, Hao Yu, and Ming Li. "ONBIRES: Ontology-Based Biological Relation Extraction System." In 4th Asia-Pacific Bioinformatics Conference. PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO., 2005. http://dx.doi.org/10.1142/9781860947292_0036.

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Soliman, Taysir Hassan A., Marwa Hussein, and Mohamed El-Sharkawi. "Mining disease integrated ontology." In 2012 IEEE 12th International Conference on Bioinformatics & Bioengineering (BIBE). IEEE, 2012. http://dx.doi.org/10.1109/bibe.2012.6399704.

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Ruebenacker, Oliver, Ion I. Moraru, James C. Schaff, and Michael L. Blinov. "Kinetic Modeling Using BioPAX Ontology." In 2007 IEEE International Conference on Bioinformatics and Biomedicine. IEEE, 2007. http://dx.doi.org/10.1109/bibm.2007.55.

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