{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:53:37Z","timestamp":1783410817742,"version":"3.54.6"},"reference-count":29,"publisher":"Oxford University Press (OUP)","issue":"18","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2015,9,15]]},"abstract":"<jats:title>Abstract<\/jats:title>\n               <jats:p>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.<\/jats:p>\n               <jats:p>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 \u2018deep learning\u2019 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.<\/jats:p>\n               <jats:p>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.<\/jats:p>\n               <jats:p>Contact: xinghua@pitt.edu<\/jats:p>\n               <jats:p>Supplementary information: \u00a0Supplementary data are available at Bioinformatics online.<\/jats:p>","DOI":"10.1093\/bioinformatics\/btv315","type":"journal-article","created":{"date-parts":[[2015,5,21]],"date-time":"2015-05-21T02:30:54Z","timestamp":1432175454000},"page":"3008-3015","source":"Crossref","is-referenced-by-count":30,"title":["Trans-species learning of cellular signaling systems with bimodal deep belief networks"],"prefix":"10.1093","volume":"31","author":[{"given":"Lujia","family":"Chen","sequence":"first","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chunhui","family":"Cai","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Vicky","family":"Chen","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xinghua","family":"Lu","sequence":"additional","affiliation":[{"name":"Department of Biomedical Informatics, University of Pittsburgh, Pittsburgh, PA 15237, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2015,5,20]]},"reference":[{"key":"2023020202222905900_btv315-B1","doi-asserted-by":"crossref","first-page":"733","DOI":"10.1002\/cyto.a.20896","article-title":"Quantifying colocalization by correlation: the Pearson correlation coefficient is superior to the Mander's overlap coefficient","volume":"77A","author":"Adler","year":"2010","journal-title":"Cytom Part A"},{"key":"2023020202222905900_btv315-B2","volume-title":"Molecular Biology of the Cell","author":"Alberts","year":"2008"},{"key":"2023020202222905900_btv315-B3","article-title":"Representation learning: a review and new perspectives","author":"Bengio","year":"2012","journal-title":"arXiv.org."},{"key":"2023020202222905900_btv315-B4","volume-title":"Pattern Recognition and Machine Learning","author":"Bishop","year":"2006"},{"key":"2023020202222905900_btv315-B5","doi-asserted-by":"crossref","first-page":"1145","DOI":"10.1016\/S0031-3203(96)00142-2","article-title":"The use of the area under the roc curve in the evaluation of machine learning algorithms","volume":"30","author":"Bradley","year":"1997","journal-title":"Pattern Recogn."},{"key":"2023020202222905900_btv315-B6","doi-asserted-by":"crossref","first-page":"864","DOI":"10.1161\/01.STR.20.7.864","article-title":"Measurements of acute cerebral infarction\u2014a clinical examination scale","volume":"20","author":"Brott","year":"1989","journal-title":"Stroke"},{"key":"2023020202222905900_btv315-B7","doi-asserted-by":"crossref","first-page":"361","DOI":"10.1007\/s00335-011-9352-4","article-title":"Disease model discovery and translation. 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