PT  - JOURNAL ARTICLE
AU  - Weihua Guo
AU  - You (Eric) Xu
AU  - Xueyang Feng
TI  - DeepMetabolism: A Deep Learning System to Predict Phenotype from Genome Sequencing
AID  - 10.1101/135574
DP  - 2017 Jan 01
TA  - bioRxiv
PG  - 135574
4099  - http://biorxiv.org/content/early/2017/05/09/135574.short
4100  - http://biorxiv.org/content/early/2017/05/09/135574.full
AB  - 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.