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

摘要: Background A living cell has a complex, hierarchically organized signaling system that encodes and assimilates diverse environmental intracellular signals, it further transmits signals control cellular responses, including tightly controlled transcriptional program. An important yet challenging task in systems biology is to reconstruct data-driven manner. In this study, we investigate the utility of deep hierarchical neural networks learning representing organization yeast transcriptomic machinery.