Transfer learning using a nonparametric sparse topic model
Neurocomputing
| Hi-index | 3.84 |
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. Contact: samuel.kaski@aalto.fi Supplementary Information:Supplementary data are available at Bioinformatics online.