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Bioinformatics. 2015 Sep 15;31(18):3008-15. doi: 10.1093/bioinformatics/btv315. Epub 2015 May 20.

Trans-species learning of cellular signaling systems with bimodal deep belief networks.

Author information

1
Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.

Abstract

MOTIVATION:

Model organisms play critical roles in biomedical research of human diseases and drug development. An imperative task is to translate information/knowledge acquired from model organisms to humans. In this study, we address a trans-species learning problem: predicting human cell responses to diverse stimuli, based on the responses of rat cells treated with the same stimuli.

RESULTS:

We hypothesized that rat and human cells share a common signal-encoding mechanism but employ different proteins to transmit signals, and we developed a bimodal deep belief network and a semi-restricted bimodal deep belief network to represent the common encoding mechanism and perform trans-species learning. These 'deep learning' models include hierarchically organized latent variables capable of capturing the statistical structures in the observed proteomic data in a distributed fashion. The results show that the models significantly outperform two current state-of-the-art classification algorithms. Our study demonstrated the potential of using deep hierarchical models to simulate cellular signaling systems.

AVAILABILITY AND IMPLEMENTATION:

The software is available at the following URL: http://pubreview.dbmi.pitt.edu/TransSpeciesDeepLearning/. The data are available through SBV IMPROVER website, https://www.sbvimprover.com/challenge-2/overview, upon publication of the report by the organizers.

CONTACT:

xinghua@pitt.edu

SUPPLEMENTARY INFORMATION:

Supplementary data are available at Bioinformatics online.

PMID:
25995230
PMCID:
PMC4668779
DOI:
10.1093/bioinformatics/btv315
[Indexed for MEDLINE]
Free PMC Article

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