Inferring Microarray Relevance By Enrichment Of Chemotherapy Resistance-Based MicroRNA Sets

Abstract

Inferring relevance between microarray experiments stored in a gene expression repository is a helpful practice for biological data mining and information retrieval studies. In this study, we propose a knowledge-based approach for representing microarray experiment content to be used in such studies. The representation scheme is specifically designed for inferring a disease-associated relevance of microRNA experiments. A group of annotated microRNA sets based on their chemotherapy resistance are used for a statistical enrichment analysis over observed expression data. A query experiment is then represented by a single dimensional vector of these enrichment statistics, instead of raw expression data. According to the results, new representation scheme can provide a better retrieval performance than traditional differential expression-based representation.