PT  - JOURNAL ARTICLE
AU  - Liu, Wenke
AU  - Wang, Xuya
AU  - Mani, D R
AU  - Fenyö, David
TI  - Modeling transcriptional profiles of gene perturbation with deep neural network
AID  - 10.1101/2021.07.15.452534
DP  - 2021 Jan 01
TA  - bioRxiv
PG  - 2021.07.15.452534
4099  - http://biorxiv.org/content/early/2021/07/16/2021.07.15.452534.short
4100  - http://biorxiv.org/content/early/2021/07/16/2021.07.15.452534.full
AB  - Background Cell line perturbation data could be utilized as a reference for inferring underlying molecular processes in new gene expression profiles. It is important to develop accurate and computationally efficient algorithms to exploit biological knowledge in the growing compendium of existing perturbation data and harness these for new predictions.Results We reframed the problem of inferring possible gene perturbation based on a reference perturbation database into a classification task and evaluated the application of deep neural network models to address this problem. Our results showed that a fully-connected multi-layer neural network was able to achieve up to 74.9% accuracy in a holdout test set, but the model generalizability was limited by consistency between training and testing data.Conclusion Capacity and flexibility enables neural network models to efficiently represent transcriptomic features associated with single gene knockdown perturbations. With consistent signals between training and testing sets, neural networks may be trained to classify new samples to experimentally confirmed molecular phenotypes.Competing Interest StatementThe authors have declared no competing interest.CMapConnectivity MapDNNDeep Neural NetworkshRNAshort hairpin RNACRISPRClustered Regularly Interspaced Short Palindromic RepeatsCGSConsensus Gene SignaturesESEnrichment ScoreWTCSWeighted Connectivity ScoreELUExponential Linear Unit