Relevant bibliographies by topics / Bioinformatics - Methodology

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

Academic literature on the topic 'Bioinformatics - Methodology'

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

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Journal articles on the topic "Bioinformatics - Methodology"

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Gasparovica-Asīte, M., and L. Aleksejeva. "Classification Methodology for Bioinformatics Data Analysis." Automatic Control and Computer Sciences 53, no. 1 (2019): 28–38. http://dx.doi.org/10.3103/s0146411619010073.

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Hauth, Amy M., and Gertraud Burger. "Methodology for Constructing Problem Definitions in Bioinformatics." Bioinformatics and Biology Insights 2 (January 2008): BBI.S706. http://dx.doi.org/10.4137/bbi.s706.

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Motivation A recurrent criticism is that certain bioinformatics tools do not account for crucial biology and therefore fail answering the targeted biological question. We posit that the single most important reason for such shortcomings is an inaccurate formulation of the computational problem. Results Our paper describes how to define a bioinformatics problem so that it captures both the underlying biology and the computational constraints for a particular problem. The proposed model delineates comprehensively the biological problem and conducts an item-by-item bioinformatics transformation resulting in a germane computational problem. This methodology not only facilitates interdisciplinary information flow but also accommodates emerging knowledge and technologies.
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Saraiya, P., C. North, and K. Duca. "An Insight-Based Methodology for Evaluating Bioinformatics Visualizations." IEEE Transactions on Visualization and Computer Graphics 11, no. 4 (2005): 443–56. http://dx.doi.org/10.1109/tvcg.2005.53.

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Zvárová, J. "IMIA Conference “Statistical Methodology in Bioinformatics and Clinical Trials”." Methods of Information in Medicine 45, no. 02 (2006): 137–38. http://dx.doi.org/10.1055/s-0038-1634056.

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Ramlo, Susan E., David McConnell, Zhong-Hui Duan, and Francisco B. Moore. "Evaluating an Inquiry-based Bioinformatics Course Using Q Methodology." Journal of Science Education and Technology 17, no. 3 (2008): 219–25. http://dx.doi.org/10.1007/s10956-008-9090-x.

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Theodosiou, T., N. Darzentas, L. Angelis, and C. A. Ouzounis. "PuReD-MCL: a graph-based PubMed document clustering methodology." Bioinformatics 24, no. 17 (2008): 1935–41. http://dx.doi.org/10.1093/bioinformatics/btn318.

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Schulz, S., E. Beisswanger, L. van den Hoek, O. Bodenreider, and E. M. van Mulligen. "Alignment of the UMLS semantic network with BioTop: methodology and assessment." Bioinformatics 25, no. 12 (2009): i69—i76. http://dx.doi.org/10.1093/bioinformatics/btp194.

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Öztürk, Hakime, Elif Ozkirimli, and Arzucan Özgür. "A novel methodology on distributed representations of proteins using their interacting ligands." Bioinformatics 34, no. 13 (2018): i295—i303. http://dx.doi.org/10.1093/bioinformatics/bty287.

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Meyer, P., J. Hoeng, J. J. Rice, et al. "Industrial methodology for process verification in research (IMPROVER): toward systems biology verification." Bioinformatics 28, no. 9 (2012): 1193–201. http://dx.doi.org/10.1093/bioinformatics/bts116.

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Tekwe, C. D., R. J. Carroll, and A. R. Dabney. "Application of survival analysis methodology to the quantitative analysis of LC-MS proteomics data." Bioinformatics 28, no. 15 (2012): 1998–2003. http://dx.doi.org/10.1093/bioinformatics/bts306.

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Dissertations / Theses on the topic "Bioinformatics - Methodology"

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Wu, Chao. "Intelligent Data Mining on Large-scale Heterogeneous Datasets and its Application in Computational Biology." University of Cincinnati / OhioLINK, 2014. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1406880774.

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Herai, Roberto Hirochi. "Metodologias de bioinformatica para detecção e estudo de sequencias repetitivas em loci genicos de transcritos quimericos." [s.n.], 2010. http://repositorio.unicamp.br/jspui/handle/REPOSIP/317152.

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Orientador: Michel Eduardo Beleza Yamagishi<br>Tese (doutorado) - Universidade Estadual de Campinas, Instituto de Biologia<br>Made available in DSpace on 2018-08-15T17:21:19Z (GMT). No. of bitstreams: 1 Herai_RobertoHirochi_D.pdf: 3625854 bytes, checksum: 3f19d10a9b0bb7f77091197cd302f66e (MD5) Previous issue date: 2010<br>Resumo: A grande quantidade de dados biológicos gerados recentemente permitiu verificar que os genomas são repletos de seqüências repetitivas (SR), como microsatélites e elementos genéticos móveis, altamente improváveis de ocorrer estatisticamente se os genomas fossem gerados a partir de uma distribuição aleatória de nucleotídeos. Tal comprovação motivou a classificação de tais seqüências e também a construção de diversas ferramentas de bioinformática, além de mecanismos de armazenamento baseados em sistemas de gerenciamento de bancos de dados (SGBD) para permitir localizá-las e armazená-las para posterior estudo. Entretanto, foi com a comprovação biológica da importância das SR, como no mecanismo de interferência por RNAi (SR reversa complementar), que as SR despertaram maior interesse por parte da comunidade científica. Atualmente, já há fortes evidências que associam as SR com fenômenos biológicos bastante interessantes, como o processamento de RNA por cis-splicing e a formação de transcritos quiméricos, freqüentes em organismos inferiores e muito raro em organismos superiores. Tais tipos de transcritos podem ser gerados a partir de trans-splicing ou, como conjecturamos nesse trabalho, pela transposição de elementos genéticos móveis (como por exemplo transposons ou retrotransposons). Em virtude disso, este projeto propõe a construção de metodologias de Bioinformática, disponibilizadas na WEB, para detectar transcritos quiméricos em genomas de organismos, tanto em versões draft ou em alta qualidade, e também estudar as SR que ocorrem no locus gênico dos transcritos envolvidos na formação de uma seqüência quimérica. As ferramentas propostas permitiram identificar, a partir de bibliotecas de transcritos de full-length cDNA, tanto de humanos quanto de bovinos, novos transcritos quiméricos provenientes de células de tecidos normais, e que não seguem splice-sites canônicos na região de fusão dos transcritos envolvidos. Além disso, as seqüências encontradas apresentam uma elevada taxa de concentração de pares de SR do tipo reverso complementar no locus gênico dos dois transcritos que formam a seqüência quimérica. As ferramentas propostas podem ser utilizadas para outros organismos e direcionar trabalhos experimentais para tentar comprovar em bancada novos transcritos quiméricos, tanto em organismos inferiores quanto em superiores<br>Abstract: The recent availability of a huge amount of biological data allowed to know about the high concentration of repetitive sequences (SR) like microsatellites and genetic mobile elements in different genomes. Repetitive sequences are improbable to occur statistically if genome data were generated by a random distribution of nucleotides. Such observation motivated the classification of repetitive sequences, and the construction of several bioinformatics tools. Furthermore, several mechanisms to store repetitive sequences, which are based on data base management systems (DBMS) were proposed and created. They can be used to search for specific sequences to make a posteriori study. However, it was with the biological confirmation of the importance of repetitive sequences, like by the RNA interference (reverse complement, or inverted repeat) mechanism, that the scientific community gained more interest by such sequences. Actually, there is strong evidence that associates the repetitive sequences with some interesting biological phenomena, like in RNA processing by cis-splicing, and in chimeric transcript formation mechanism. This last one is very frequently in inferior organism, but rare in superior organisms. Such types of transcripts can be generated by trans-splicing, or like conjectured in this work, by the retrotransposition of mobile genetic elements (like transposons or retrotransposons). In this way, this work proposed the construction of several Bioinformatics methodologies, available in the WEB, to detect new evidences of chimeric transcripts in genomes of different organisms, both in draft genome and in high quality genome assemblage. We also studied repetitive sequences in gene loci of the involved transcripts in a chimeric sequence formation. The proposed tools allowed us to identify, using a full-length cDNA databank, new chimeric transcript candidates in human and in bovine genome. They are from cells of normal tissues, and do not follow canonical splice-sites in the fusion region of the involved transcripts. Moreover, it was possible to show that the detected sequences have high concentration pairs of reverse complement type of repetitive sequences in gene loci of the two involved transcripts, which originated a new chimeric transcript candidate. The created bioinformatics tools can be used in other organisms in addition to the one used in this work, leading to the proposition of new experimental work to try to prove in vivo new chimeric transcripts, both in superior organism and in inferior organism<br>Doutorado<br>Bioinformatica<br>Doutor em Genetica e Biologia Molecular
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Zhuang, Jiali. "Structural Variation Discovery and Genotyping from Whole Genome Sequencing: Methodology and Applications: A Dissertation." eScholarship@UMMS, 2009. http://escholarship.umassmed.edu/gsbs_diss/875.

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A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.
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Zhuang, Jiali. "Structural Variation Discovery and Genotyping from Whole Genome Sequencing: Methodology and Applications: A Dissertation." eScholarship@UMMS, 2015. https://escholarship.umassmed.edu/gsbs_diss/875.

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A comprehensive understanding about how genetic variants and mutations contribute to phenotypic variations and alterations entails experimental technologies and analytical methodologies that are able to detect genetic variants/mutations from various biological samples in a timely and accurate manner. High-throughput sequencing technology represents the latest achievement in a series of efforts to facilitate genetic variants discovery and genotyping and promises to transform the way we tackle healthcare and biomedical problems. The tremendous amount of data generated by this new technology, however, needs to be processed and analyzed in an accurate and efficient way in order to fully harness its potential. Structural variation (SV) encompasses a wide range of genetic variations with different sizes and generated by diverse mechanisms. Due to the technical difficulties of reliably detecting SVs, their characterization lags behind that of SNPs and indels. In this dissertation I presented two novel computational methods: one for detecting transposable element (TE) transpositions and the other for detecting SVs in general using a local assembly approach. Both methods are able to pinpoint breakpoint junctions at single-nucleotide resolution and estimate variant allele frequencies in the sample. I also applied those methods to study the impact of TE transpositions on the genomic stability, the inheritance patterns of TE insertions in the population and the molecular mechanisms and potential functional consequences of somatic SVs in cancer genomes.
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Yngman, Gunnar. "Individualization of fixed-dose combination regimens : Methodology and application to pediatric tuberculosis." Thesis, Uppsala universitet, Institutionen för farmaceutisk biovetenskap, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-242059.

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Introduction: No Fixed-Dose Combination (FDC) formulations currently exist for pediatric tuberculosis (TB) treatment. Earlier work implemented, in the software NONMEM, a rational method for optimizing design and individualization of pediatric anti-TB FDC formulations based on patient body weight, but issues with parameter estimation, dosage strata heterogeneity and representative pharmacokinetics remained. Aim: To further develop the rational model-based methodology aiding the selection of appropriate FDC formulation designs and dosage regimens, in pediatric TB treatment. Materials and Methods: Optimization of the method with respect to the estimation of body weight breakpoints was sought. Heterogeneity of dosage groups with respect to treatment efficiency was sought to be improved. Recently published pediatric pharmacokinetic parameters were implemented and the model translated to MATLAB, where also the performance was evaluated by stochastic estimation and graphical visualization. Results: A logistic function was found better suited as an approximation of breakpoints. None of the estimation methods implemented in NONMEM were more suitable than the originally used FO method. Homogenization of dosage group treatment efficiency could not be solved. MATLAB translation was successful but required stochastic estimations and highlighted high densities of local minima. Representative pharmacokinetics were successfully implemented. Conclusions: NONMEM was found suboptimal for the task due to problems with discontinuities and heterogeneity, but a stepwise method with representative pharmacokinetics were successfully implemented. MATLAB showed more promise in the search for a method also addressing the heterogeneity issue.
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Graversen, Therese. "Statistical and computational methodology for the analysis of forensic DNA mixtures with artefacts." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:4c3bfc88-25e7-4c5b-968f-10a35f5b82b0.

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This thesis proposes and discusses a statistical model for interpreting forensic DNA mixtures. We develop methods for estimation of model parameters and assessing the uncertainty of the estimated quantities. Further, we discuss how to interpret the mixture in terms of predicting the set of contributors. We emphasise the importance of challenging any interpretation of a particular mixture, and for this purpose we develop a set of diagnostic tools that can be used in assessing the adequacy of the model to the data at hand as well as in a systematic validation of the model on experimental data. An important feature of this work is that all methodology is developed entirely within the framework of the adopted model, ensuring a transparent and consistent analysis. To overcome the challenge that lies in handling the large state space for DNA profiles, we propose a representation of a genotype that exhibits a Markov structure. Further, we develop methods for efficient and exact computation in a Bayesian network. An implementation of the model and methodology is available through the R package DNAmixtures.
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Forest, Marie. "Simultaneous estimation of population size changes and splits times using importance sampling." Thesis, University of Oxford, 2014. http://ora.ox.ac.uk/objects/uuid:8c067a3d-44d5-468a-beb5-34c5830998c4.

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The genome is a treasure trove of information about the history of an individual, his population, and his species. For as long as genomic data have been available, methods have been developed to retrieve this information and learn about population history. Over the last decade, large international genomic projects (e.g. the HapMap Project and the 1000 Genomes Project) have offered access to high quality data collected from thousands of individuals from a vast number of populations. Freely available to all, these databases offer the possibility to develop new methods to uncover the history of the peopling of the world by modern humans. Due to the complexity of the problem and the large amount of available data, all developed methods either simplify the model with strong assumptions or use an approximation; they also dramatically down-sample their data by either using fewer individuals or only portions of the genome. In this thesis, we present a novel method to jointly estimate the time of divergence of a pair of populations and their variable sizes, a previously unsolved problem. The method uses multiple regions of the genome with low recombination rate. For each region, we use an importance sampler to build a large number of possible genealogies, and from those we estimate the likelihood function of parameters of interest. By modelling the population sizes as piecewise constant within fixed time intervals, we aim to capture population size variation through time. We show via simulation studies that the method performs well in many situations, even when the model assumptions are not totally met. We apply the method to five populations from the 1000 Genomes Project, obtaining estimates of split times between European groups and among Europe, Africa and Asia. We also infer shared and non-shared bottlenecks in out-of- Africa groups, expansions following population separations, and the sizes of ancestral populations further back in time.
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Liu, Wanting. "An Integrated Bioinformatics Approach for the Identification of Melanoma-Associated Biomarker Genes. A Ranking and Stratification Approach as a New Meta-Analysis Methodology for the Detection of Robust Gene Biomarker Signatures of Cancers." Thesis, University of Bradford, 2014. http://hdl.handle.net/10454/7346.

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Genome-wide microarray technology has facilitated the systematic discovery of diagnostic biomarkers of cancers and other pathologies. However, meta-analyses of published arrays using melanoma as a test cancer has uncovered significant inconsistences that hinder advances in clinical practice. In this study a computational model for the integrated analysis of microarray datasets is proposed in order to provide a robust ranking of genes in terms of their relative significance; both genome-wide relative significance (GWRS) and genome-wide global significance (GWGS). When applied to five melanoma microarray datasets published between 2000 and 2011, a new 12-gene diagnostic biomarker signature for melanoma was defined (i.e., EGFR, FGFR2, FGFR3, IL8, PTPRF, TNC, CXCL13, COL11A1, CHP2, SHC4, PPP2R2C, and WNT4). Of these, CXCL13, COL11A1, PTPRF and SHC4 are components of the MAPK pathway and were validated by immunocyto- and immunohisto-chemistry. These proteins were found to be overexpressed in metastatic and primary melanoma cells in vitro and in melanoma tissue in situ compared to melanocytes cultured from healthy skin epidermis and normal healthy human skin. One challenge for the integrated analysis of microarray data is that the microarray data are produced using different platforms and bio-samples, e.g. including both cell line- and biopsy-based microarray datasets. In order to address these challenges, the computational model was further enhanced the stratification of datasets into either biopsy or cell line derived datasets, and via the weighting of microarray data based on quality criteria of data. The methods enhancement was applied to 14 microarray datasets of three cancers (breast, prostate, and melanoma) based on classification accuracy and on the capability to identify predictive biomarkers. Four novel measures for evaluating the capability to identify predictive biomarkers are proposed: (1) classifying independent testing data using wrapper feature selection with machine leaning, (2) assessing the number of common genes with the genes retrieved in independent testing data, (3) assessing the number of common genes with the genes retrieved in across multiple training datasets, (4) assessing the number of common genes with the genes validated in the literature. This enhancement of computational approach (i) achieved reliable classification performance across multiple datasets, (ii) recognized more significant genes into the top-ranked genes as compared to the genes detected by the independent test data, and (iii) detected more meaningful genes than were validated in previous melanoma studies in the literature.
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Czerwińska, Urszula. "Unsupervised deconvolution of bulk omics profiles : methodology and application to characterize the immune landscape in tumors Determining the optimal number of independent components for reproducible transcriptomic data analysis Application of independent component analysis to tumor transcriptomes reveals specific and reproducible immune-related signals A multiscale signalling network map of innate immune response in cancer reveals signatures of cell heterogeneity and functional polarization." Thesis, Sorbonne Paris Cité, 2018. http://www.theses.fr/2018USPCB075.

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Les tumeurs sont entourées d'un microenvironnement complexe comprenant des cellules tumorales, des fibroblastes et une diversité de cellules immunitaires. Avec le développement actuel des immunothérapies, la compréhension de la composition du microenvironnement tumoral est d'une importance critique pour effectuer un pronostic sur la progression tumorale et sa réponse au traitement. Cependant, nous manquons d'approches quantitatives fiables et validées pour caractériser le microenvironnement tumoral, facilitant ainsi le choix de la meilleure thérapie. Une partie de ce défi consiste à quantifier la composition cellulaire d'un échantillon tumoral (appelé problème de déconvolution dans ce contexte), en utilisant son profil omique de masse (le profil quantitatif global de certains types de molécules, tels que l'ARNm ou les marqueurs épigénétiques). La plupart des méthodes existantes utilisent des signatures prédéfinies de types cellulaires et ensuite extrapolent cette information à des nouveaux contextes. Cela peut introduire un biais dans la quantification de microenvironnement tumoral dans les situations où le contexte étudié est significativement différent de la référence. Sous certaines conditions, il est possible de séparer des mélanges de signaux complexes, en utilisant des méthodes de séparation de sources et de réduction des dimensions, sans définitions de sources préexistantes. Si une telle approche (déconvolution non supervisée) peut être appliquée à des profils omiques de masse de tumeurs, cela permettrait d'éviter les biais contextuels mentionnés précédemment et fournirait un aperçu des signatures cellulaires spécifiques au contexte. Dans ce travail, j'ai développé une nouvelle méthode appelée DeconICA (Déconvolution de données omiques de masse par l'analyse en composantes immunitaires), basée sur la méthodologie de séparation aveugle de source. DeconICA a pour but l'interprétation et la quantification des signaux biologiques, façonnant les profils omiques d'échantillons tumoraux ou de tissus normaux, en mettant l'accent sur les signaux liés au système immunitaire et la découverte de nouvelles signatures. Afin de rendre mon travail plus accessible, j'ai implémenté la méthode DeconICA en tant que librairie R. En appliquant ce logiciel aux jeux de données de référence, j'ai démontré qu'il est possible de quantifier les cellules immunitaires avec une précision comparable aux méthodes de pointe publiées, sans définir a priori des gènes spécifiques au type cellulaire. DeconICA peut fonctionner avec des techniques de factorisation matricielle telles que l'analyse indépendante des composants (ICA) ou la factorisation matricielle non négative (NMF). Enfin, j'ai appliqué DeconICA à un grand volume de données : plus de 100 jeux de données, contenant au total plus de 28 000 échantillons de 40 types de tumeurs, générés par différentes technologies et traités indépendamment. Cette analyse a démontré que les signaux immunitaires basés sur l'ICA sont reproductibles entre les différents jeux de données. D'autre part, nous avons montré que les trois principaux types de cellules immunitaires, à savoir les lymphocytes T, les lymphocytes B et les cellules myéloïdes, peuvent y être identifiés et quantifiés. Enfin, les métagènes dérivés de l'ICA, c'est-à-dire les valeurs de projection associées à une source, ont été utilisés comme des signatures spécifiques permettant d'étudier les caractéristiques des cellules immunitaires dans différents types de tumeurs. L'analyse a révélé une grande diversité de phénotypes cellulaires identifiés ainsi que la plasticité des cellules immunitaires, qu'elle soit dépendante ou indépendante du type de tumeur. Ces résultats pourraient être utilisés pour identifier des cibles médicamenteuses ou des biomarqueurs pour l'immunothérapie du cancer<br>Tumors are engulfed in a complex microenvironment (TME) including tumor cells, fibroblasts, and a diversity of immune cells. Currently, a new generation of cancer therapies based on modulation of the immune system response is in active clinical development with first promising results. Therefore, understanding the composition of TME in each tumor case is critically important to make a prognosis on the tumor progression and its response to treatment. However, we lack reliable and validated quantitative approaches to characterize the TME in order to facilitate the choice of the best existing therapy. One part of this challenge is to be able to quantify the cellular composition of a tumor sample (called deconvolution problem in this context), using its bulk omics profile (global quantitative profiling of certain types of molecules, such as mRNA or epigenetic markers). In recent years, there was a remarkable explosion in the number of methods approaching this problem in several different ways. Most of them use pre-defined molecular signatures of specific cell types and extrapolate this information to previously unseen contexts. This can bias the TME quantification in those situations where the context under study is significantly different from the reference. In theory, under certain assumptions, it is possible to separate complex signal mixtures, using classical and advanced methods of source separation and dimension reduction, without pre-existing source definitions. If such an approach (unsupervised deconvolution) is feasible to apply for bulk omic profiles of tumor samples, then this would make it possible to avoid the above mentioned contextual biases and provide insights into the context-specific signatures of cell types. In this work, I developed a new method called DeconICA (Deconvolution of bulk omics datasets through Immune Component Analysis), based on the blind source separation methodology. DeconICA has an aim to decipher and quantify the biological signals shaping omics profiles of tumor samples or normal tissues. A particular focus of my study was on the immune system-related signals and discovering new signatures of immune cell types. In order to make my work more accessible, I implemented the DeconICA method as an R package named "DeconICA". By applying this software to the standard benchmark datasets, I demonstrated that DeconICA is able to quantify immune cells with accuracy comparable to published state-of-the-art methods but without a priori defining a cell type-specific signature genes. The implementation can work with existing deconvolution methods based on matrix factorization techniques such as Independent Component Analysis (ICA) or Non-Negative Matrix Factorization (NMF). Finally, I applied DeconICA to a big corpus of data containing more than 100 transcriptomic datasets composed of, in total, over 28000 samples of 40 tumor types generated by different technologies and processed independently. This analysis demonstrated that ICA-based immune signals are reproducible between datasets and three major immune cell types: T-cells, B-cells and Myeloid cells can be reliably identified and quantified. Additionally, I used the ICA-derived metagenes as context-specific signatures in order to study the characteristics of immune cells in different tumor types. The analysis revealed a large diversity and plasticity of immune cells dependent and independent on tumor type. Some conclusions of the study can be helpful in identification of new drug targets or biomarkers for immunotherapy of cancer
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Prabhu, Snehit. "Computational Contributions Towards Scalable and Efficient Genome-wide Association Methodology." Thesis, 2013. https://doi.org/10.7916/D8R78NF0.

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Genome-wide association studies are experiments designed to find the genetic bases of physical traits: for example, markers correlated with disease status by comparing the DNA of healthy individuals to the DNA of affecteds. Over the past two decades, an exponential increase in the resolution of DNA-testing technology coupled with a substantial drop in their cost have allowed us to amass huge and potentially invaluable datasets to conduct such comparative studies. For many common diseases, datasets as large as a hundred thousand individuals exist, each tested at million(s) of markers (called SNPs) across the genome. Despite this treasure trove, so far only a small fraction of the genetic markers underlying most common diseases have been identified. Simply stated - our ability to predict phenotype (disease status) from a person's genetic constitution is still very limited today, even for traits that we know to be heritable from one's parents (e.g. height, diabetes, cardiac health). As a result, genetics today often lags far behind conventional indicators like family history of disease in terms of its predictive power. To borrow a popular metaphor from astronomy, this veritable "dark matter" of perceivable but un-locatable genetic signal has come to be known as missing heritability. This thesis will present my research contributions in two hotly pursued scientific hypotheses that aim to close this gap: (1) gene-gene interactions, and (2) ultra-rare genetic variants - both of which are not yet widely tested. First, I will discuss the challenges that have made interaction testing difficult, and present a novel approximate statistic to measure interaction. This statistic can be exploited in a Monte-Carlo like randomization scheme, making an exhaustive search through trillions of potential interactions tractable using ordinary desktop computers. A software implementation of our algorithm found a reproducible interaction between SNPs in two calcium channel genes in Bipolar Disorder. Next, I will discuss the functional enrichment pipeline we subsequently developed to identify sets of interacting genes underlying this disease. Lastly, I will talk about the application of coding theory to cost-efficient measurement of ultra-rare genetic variation (sometimes, as rare as just one individual carrying the mutation in the entire population).
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Books on the topic "Bioinformatics - Methodology"

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Bioinformatics and systems biology. Springer, 2008.

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Narayanan, P. Bioinformatics: A primer. New Age International (P) Ltd., Publishers, 2005.

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Sharma, Kal Renganathan. Bioinformatics. McGraw-Hill, 2008.

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Seifert, D. Bioinformatics methods and protocols. Humana, 2010.

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

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Okun, Oleg. Feature selection and ensemble methods for bioinformatics: Algorithmic classification and implementations. Medical Information Science Reference, 2011.

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Gupal, V. I. Matematicheskie metody analiza i raspoznavanii︠a︡ geneticheskoĭ informat︠s︡ii: Monografii︠a︡. RIOR, 2012.

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Henryk, Maciejewski. Predictive modelling in high-dimensional data: Prior domain knowledge-based approaches. Oficyna Wydawnicza Politechniki Wrocławskiej, 2013.

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Bioinformatics: Sequence alignment and Markov models. McGraw-Hill, 2009.

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Cesario, Alfredo. Cancer Systems Biology, Bioinformatics and Medicine: Research and Clinical Applications. Springer Science+Business Media B.V., 2011.

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Book chapters on the topic "Bioinformatics - Methodology"

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Campbell, Colin. "Machine Learning Methodology in Bioinformatics." In Springer Handbook of Bio-/Neuroinformatics. Springer Berlin Heidelberg, 2014. http://dx.doi.org/10.1007/978-3-642-30574-0_12.

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Aung, Yan Lin, Douglas L. Maskell, Timothy F. Oliver, Bertil Schmidt, and William Bong. "C-Based Design Methodology for FPGA Implementation of ClustalW MSA." In Pattern Recognition in Bioinformatics. Springer Berlin Heidelberg, 2007. http://dx.doi.org/10.1007/978-3-540-75286-8_2.

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Varma, B. Sharat Chandra, Kolin Paul, and M. Balakrishnan. "Methodology for Implementing Accelerators." In Architecture Exploration of FPGA Based Accelerators for BioInformatics Applications. Springer Singapore, 2016. http://dx.doi.org/10.1007/978-981-10-0591-6_3.

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Lancashire, Lee, and Graham Ball. "Computational and Statistical Methodologies for Data Mining in Bioinformatics." In Key Topics in Surgical Research and Methodology. Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-540-71915-1_27.

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Amidi, Afshine, Shervine Amidi, Dimitrios Vlachakis, Nikos Paragios, and Evangelia I. Zacharaki. "A Machine Learning Methodology for Enzyme Functional Classification Combining Structural and Protein Sequence Descriptors." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-31744-1_63.

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Harari, Oscar, Luis Herrera, and Igor Zwir. "A Hybrid Promoter Analysis Methodology for Prokaryotic Genomes." In Fuzzy Systems in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-540-89968-6_3.

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Cortés-Ancos, Estefanía, Manuel Emilio Gegúndez-Arias, and Diego Marin. "Microaneurysm Candidate Extraction Methodology in Retinal Images for the Integration into Classification-Based Detection Systems." In Bioinformatics and Biomedical Engineering. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-56148-6_33.

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dos Reis, Adriana Neves, and Ney Lemke. "An Improved Hidden Markov Model Methodology to Discover Prokaryotic Promoters." In Advances in Bioinformatics and Computational Biology. Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11532323_10.

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Costa, Marina C., André F. Gabriel, and Francisco J. Enguita. "Bioinformatics Research Methodology of Non-coding RNAs in Cardiovascular Diseases." In Advances in Experimental Medicine and Biology. Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-1671-9_2.

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Lo Bosco, Giosuè, and Luca Pinello. "A New Feature Selection Methodology for K-mers Representation of DNA Sequences." In Computational Intelligence Methods for Bioinformatics and Biostatistics. Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-24462-4_9.

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Conference papers on the topic "Bioinformatics - Methodology"

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Li, Lei, Roop G. Singh, Guangzhi Zheng, Art Vandenberg, Vijay Vaishnavi, and Sham Navathe. "A methodology for semantic integration of metadata in bioinformatics data sources." In the 43rd annual southeast regional conference. ACM Press, 2005. http://dx.doi.org/10.1145/1167350.1167393.

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Fiannaca, A., M. La Rosa, Riccardo Rizzo, Alfonso Urso, and Salvatore Gaglio. "An ontology design methodology for Knowledge-Based systems with application to bioinformatics." In 2012 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology (CIBCB). IEEE, 2012. http://dx.doi.org/10.1109/cibcb.2012.6217215.

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Hee-Jeong Jin, Eun-Soo Jang, and Si-Woo Lee. "Clinical network for case based reasoning methodology." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112518.

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Bin Hu. "Computational psychophysiology based research methodology for mental health." In 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2016. http://dx.doi.org/10.1109/bibm.2016.7822474.

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Sobczak, N. L., G. F. Corliss, M. A. Seitz, and P. J. Tonellato. "Cluster Methodology Defines Archetype Sentinel Consomic Rats." In 2007 4th Symposium on Computational Intelligence in Bioinformatics and Computational Biology. IEEE, 2007. http://dx.doi.org/10.1109/cibcb.2007.4221204.

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Karargyris, Alexandros, and Nikolaos Bourbakis. "An Elastic Video Interpolation Methodology for Wireless Capsule Endoscopy Videos." In 2010 IEEE International Conference on BioInformatics and BioEngineering. IEEE, 2010. http://dx.doi.org/10.1109/bibe.2010.16.

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Zhao, Tiantao, Lijie Zhang, and Youcai Zhao. "Study on Biomethane Inhibition Using Response Surface Methodology." In 2009 3rd International Conference on Bioinformatics and Biomedical Engineering (iCBBE). IEEE, 2009. http://dx.doi.org/10.1109/icbbe.2009.5163463.

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Xiaodong Zhou and E. O. George. "An improved data integration methodology for system biology." In 2011 IEEE International Conference on Bioinformatics and Biomedicine Workshops (BIBMW). IEEE, 2011. http://dx.doi.org/10.1109/bibmw.2011.6112380.

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Chu, Na, and Jie Li. "Methodology study of classification algorithm in TCM ZHENG diagnosis." In 2014 IEEE International Conference on Bioinformatics and Biomedicine (BIBM). IEEE, 2014. http://dx.doi.org/10.1109/bibm.2014.6999315.

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Soewito, Benfano, and Ning Weng. "Methodology for Evaluating DNA Pattern Searching Algorithms on Multiprocessor." In 2007 IEEE 7th International Symposium on BioInformatics and BioEngineering. IEEE, 2007. http://dx.doi.org/10.1109/bibe.2007.4375618.

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