Trans-species learning of cellular signaling systems with bimodal deep belief networks
Lujia Chen, Chunhui Cai, Vicky Chen and Xinghua Lu. Trans-species learning of cellular signaling systems with bimodal deep belief networks. Bioinformatics 2015;
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 (bDBN) and a semi-restricted bimodal deep belief network (sbDBN) to represent the common encoding mechanism and perform transspecies 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: 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.