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

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• 作者：Lujia Chen ; Chunhui Cai ; Vicky Chen ; Xinghua Lu
• 刊名：BMC Bioinformatics
• 出版年：2016
• 出版时间：December 2016
• 年：2016
• 卷：17
• 期：1-supp
• 全文大小：1,226 KB
• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105
• 卷排序：17

文摘

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.