PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Data-Driven Information Retrieval in Heterogeneous Collections of Transcriptomics Data Links SIM2s to Malignant Pleural Mesothelioma
Jose Caldas, Nils Gehlenborg, Eeva Kettunen, Ali Faisal, Mikko Rönty, Andrew Nicholson, Sakari Knuutila, Alvis Brazma and Samuel Kaski
Bioinformatics Volume 28, pp. 246-253, 2012.

Abstract

Motivation: Genome-wide measurement of transcript levels is an ubiquitous tool in biomedical research. As experimental data continues to be deposited in public databases, it is becoming important to develop search engines that enable the retrieval of relevant studies given a query study. While retrieval systems based on meta-data already exist, data-driven approaches that retrieve studies based on similarities in the expression data itself have a greater potential of uncovering novel biological insights. Results: We propose an information retrieval method based on differential expression. Our method deals with arbitrary experimental designs and performs competitively with alternative approaches, while making the search results interpretable in terms of differential expression patterns. We show that our model yields meaningful connections between biological conditions from different studies. Finally, we validate a previously unknown connection between malignant pleural mesothelioma and SIM2s suggested by our method, via real-time polymerase chain reaction in an independent set of mesothelioma samples. Availability: Supplementary data and source code are available from http://www.ebi.ac.uk/fg/research/rex.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:User Modelling for Computer Human Interaction
Learning/Statistics & Optimisation
Information Retrieval & Textual Information Access
ID Code:9168
Deposited By:Samuel Kaski
Deposited On:21 February 2012