title: Trans-species learning of cellular signaling systems with bimodal deep belief networks.
abstract: BACKGROUND
  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.
  
  
  METHODS
  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.
  
  
  BACKGROUND
  xinghua@pitt.edu
  
  
  BACKGROUND
  Supplementary data are available at Bioinformatics online.
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.
authors.forename: Lujia
authors.initials: L
authors.lastname: Chen
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.
authors.forename: Chunhui
authors.initials: C
authors.lastname: Cai
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.
authors.forename: Vicky
authors.initials: V
authors.lastname: Chen
authors.affiliation: Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA.
authors.forename: Xinghua
authors.initials: X
authors.lastname: Lu
mesh_terms: D000465:Algorithms; D000818:Animals; D001980:Bronchi / Q000166:cytology / Q000378:metabolism*; D006801:Humans; D008962:Models, Theoretical; D010766:Phosphorylation; D060066:Protein Interaction Maps; D040901:Proteomics / Q000379:methods*; D051381:Rats; D015398:Signal Transduction; D012984:Software; D013045:Species Specificity; D049490:Systems Biology / Q000379:methods*