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Life science is entering a new era of petabyte-level sequencing data.Converting such big data to biological insights represents a huge challenge forcomputational analysis. To this end, we developed DeepMetabolism, abiology-guided deep learning system to predict cell phenotypes fromtranscriptomics data. By integrating unsupervised pre-training with supervisedtraining, 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 highrobustness (tolerate up to 75% noise). We envision DeepMetabolism to bridge thegap between genotype and phenotype and to serve as a springboard forapplications in synthetic biology and precision medicine.

DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing