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dc.contributor.advisorAgarwal, Shivani
dc.contributor.authorLaha, Anirban
dc.date.accessioned2017-12-05T16:42:18Z
dc.date.accessioned2018-07-31T04:38:44Z
dc.date.available2017-12-05T16:42:18Z
dc.date.available2018-07-31T04:38:44Z
dc.date.issued2017-12-05
dc.date.submitted2013
dc.identifier.urihttp://etd.iisc.ac.in/handle/2005/2866
dc.identifier.abstracthttp://etd.iisc.ac.in/static/etd/abstracts/3725/G26678-Abs.pdfen_US
dc.description.abstractGene prioritization involves ranking genes by possible relevance to a disease of interest. This is important in order to narrow down the set of genes to be investigated biologically, and over the years, several computational approaches have been proposed for automat-ically prioritizing genes using some form of gene-related data, mostly using statistical or machine learning methods. Recently, Agarwal and Sengupta (2009) proposed the use of learning-to-rank methods, which have been used extensively in information retrieval and related fields, to learn a ranking of genes from a given data source, and used this approach to successfully identify novel genes related to leukemia and colon cancer using only gene expression data. In this work, we explore the possibility of combining such learning-to-rank methods with rank aggregation techniques to learn a ranking of genes from multiple heterogeneous data sources, such as gene expression data, gene ontology data, protein-protein interaction data, etc. Rank aggregation methods have their origins in voting theory, and have been used successfully in meta-search applications to aggregate webpage rankings from different search engines. Here we use graph-based learning-to-rank methods to learn a ranking of genes from each individual data source represented as a graph, and then apply rank aggregation methods to aggregate these rankings into a single ranking over the genes. The thesis describes our approach, reports experiments with various data sets, and presents our findings and initial conclusions.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG26678en_US
dc.subjectGene Prioritizationen_US
dc.subjectGene Rankingen_US
dc.subjectBipartite Rankingen_US
dc.subjectLearning To Ranken_US
dc.subjectRank Aggregation Methodsen_US
dc.subjectBipartite Instance Rankingen_US
dc.subjectRank Aggregrationen_US
dc.subjectRanking of Genesen_US
dc.subjectGene Data Sourcesen_US
dc.subjectGenes Bipartite Rankingen_US
dc.subjectBipartite Graph Rankingen_US
dc.subject.classificationBioinformaticsen_US
dc.titleMachine Learning and Rank Aggregation Methods for Gene Prioritization from Heterogeneous Data Sourcesen_US
dc.typeThesisen_US
dc.degree.nameMSc Enggen_US
dc.degree.levelMastersen_US
dc.degree.disciplineFaculty of Engineeringen_US


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