  Life science is entering a new era of petabyte-level sequencing data.
Converting such big data to biological insights represents a huge challenge for
computational analysis. To this end, we developed DeepMetabolism, a
biology-guided deep learning system to predict cell phenotypes from
transcriptomics data. By integrating unsupervised pre-training with supervised
training, DeepMetabolism is able to predict phenotypes with high accuracy
(PCC>0.92), high speed (<30 min for >100 GB data using a single GPU), and high
robustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge the
gap between genotype and phenotype and to serve as a springboard for
applications in synthetic biology and precision medicine.
