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.
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.
Authors: Carine Poussin; Carole Mathis; Leonidas G Alexopoulos; Dimitris E Messinis; Rémi H J Dulize; Vincenzo Belcastro; Ioannis N Melas; Theodore Sakellaropoulos; Kahn Rhrissorrakrai; Erhan Bilal; Pablo Meyer; Marja Talikka; Stéphanie Boué; Raquel Norel; John J Rice; Gustavo Stolovitzky; Nikolai V Ivanov; Manuel C Peitsch; Julia Hoeng Journal: Sci Data Date: 2014-06-10 Impact factor: 6.444
Authors: Travers Ching; Daniel S Himmelstein; Brett K Beaulieu-Jones; Alexandr A Kalinin; Brian T Do; Gregory P Way; Enrico Ferrero; Paul-Michael Agapow; Michael Zietz; Michael M Hoffman; Wei Xie; Gail L Rosen; Benjamin J Lengerich; Johnny Israeli; Jack Lanchantin; Stephen Woloszynek; Anne E Carpenter; Avanti Shrikumar; Jinbo Xu; Evan M Cofer; Christopher A Lavender; Srinivas C Turaga; Amr M Alexandari; Zhiyong Lu; David J Harris; Dave DeCaprio; Yanjun Qi; Anshul Kundaje; Yifan Peng; Laura K Wiley; Marwin H S Segler; Simina M Boca; S Joshua Swamidass; Austin Huang; Anthony Gitter; Casey S Greene Journal: J R Soc Interface Date: 2018-04 Impact factor: 4.293