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

Chen, L and Cai, C and Chen, V and Lu, X (2015) Trans-species learning of cellular signaling systems with bimodal deep belief networks. In: UNSPECIFIED.

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Abstract

© 2015 The Author. 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.


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Details

Item Type: Conference or Workshop Item (UNSPECIFIED)
Status: Published
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Chen, L
Cai, Cchunhuic@pitt.eduCHUNHUIC
Chen, V
Lu, Xxinghua@pitt.eduXINGHUA
Date: 31 July 2015
Date Type: Publication
Journal or Publication Title: Bioinformatics
Volume: 31
Number: 18
Page Range: 3008 - 3015
DOI or Unique Handle: 10.1093/bioinformatics/btv315
Schools and Programs: School of Medicine > Biomedical Informatics
Refereed: No
ISSN: 1367-4803
Date Deposited: 22 Nov 2016 17:55
Last Modified: 29 Jan 2019 15:55
URI: http://d-scholarship.pitt.edu/id/eprint/30141

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