Learning a hierarchical representation of the yeast transcriptomic machinery using an autoencoder model

BMC Bioinformatics

Proceedings

Lujia Chen¹  Vicky Chen¹  Chunhui Cai¹  Xinghua Lu¹ 

[1] Department of Biomedical Informatics, University of Pittsburgh, 5607 Baum Blvd, 15237, Pittsburgh, PA, USA;

关键词: Yeast;    Transcription;    Gene expression;    Transcriptomic machinery;    Signal transduction;    Deep learning;    Deep hierarchical neural network;    Unsupervised learning;    Data mining;   

DOI  :  10.1186/s12859-015-0852-1

来源: Springer

【 摘 要 】

BackgroundA living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental and intracellular signals, and it further transmits signals that control cellular responses, including a tightly controlled transcriptional program. An important and yet challenging task in systems biology is to reconstruct cellular signaling system in a data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks in learning and representing the hierarchical organization of yeast transcriptomic machinery.ResultsWe have designed a sparse autoencoder model consisting of a layer of observed variables and four layers of hidden variables. We applied the model to over a thousand of yeast microarrays to learn the encoding system of yeast transcriptomic machinery. After model selection, we evaluated whether the trained models captured biologically sensible information. We show that the latent variables in the first hidden layer correctly captured the signals of yeast transcription factors (TFs), obtaining a close to one-to-one mapping between latent variables and TFs. We further show that genes regulated by latent variables at higher hidden layers are often involved in a common biological process, and the hierarchical relationships between latent variables conform to existing knowledge. Finally, we show that information captured by the latent variables provide more abstract and concise representations of each microarray, enabling the identification of better separated clusters in comparison to gene-based representation.ConclusionsContemporary deep hierarchical latent variable models, such as the autoencoder, can be used to partially recover the organization of transcriptomic machinery.

【 授权许可】

Unknown   
© Chen et al. 2016. This article is published under license to BioMed Central Ltd. Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.