Abstract
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.
Copyright
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Posted May 09, 2017.
DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing
