Machine Learning and Rank Aggregation Methods for Gene Prioritization from Heterogeneous Data Sources

• Main Author: Laha, Anirban
• Other Authors: Agarwal, Shivani
• Language: en_US
• Published: 2017
• Subjects: Gene Prioritization; Gene Ranking; Bipartite Ranking; Learning To Rank; Rank Aggregation Methods; Bipartite Instance Ranking; Rank Aggregration; Ranking of Genes; Gene Data Sources; Genes Bipartite Ranking; Bipartite Graph Ranking; Bioinformatics
• Online Access: http://hdl.handle.net/2005/2866; http://etd.ncsi.iisc.ernet.in/abstracts/3725/G26678-Abs.pdf

Gene 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.