Using interpretable deep learning to model cancer dependencies

Chih-Hsu Lin; Olivier Lichtarge

doi:10.1093/bioinformatics/btab137

Using interpretable deep learning to model cancer dependencies

Bioinformatics. 2021 Sep 9;37(17):2675-2681. doi: 10.1093/bioinformatics/btab137.

Authors

Chih-Hsu Lin¹, Olivier Lichtarge^{1

2

3

4}

Affiliations

¹ Department of Molecular and Human Genetics.
² Department of Biochemistry and Molecular Biology.
³ Department of Pharmacology.
⁴ Computational and Integrative Biomedical Research Center, Baylor College of Medicine, One Baylor Plaza, Houston, TX 77030, USA.

Abstract

Motivation: Cancer dependencies provide potential drug targets. Unfortunately, dependencies differ among cancers and even individuals. To this end, visible neural networks (VNNs) are promising due to robust performance and the interpretability required for the biomedical field.

Results: We design Biological visible neural network (BioVNN) using pathway knowledge to predict cancer dependencies. Despite having fewer parameters, BioVNN marginally outperforms traditional neural networks (NNs) and converges faster. BioVNN also outperforms an NN based on randomized pathways. More importantly, dependency predictions can be explained by correlating with the neuron output states of relevant pathways, which suggest dependency mechanisms. In feature importance analysis, BioVNN recapitulates known reaction partners and proposes new ones. Such robust and interpretable VNNs may facilitate the understanding of cancer dependency and the development of targeted therapies.

Availability and implementation: Code and data are available at https://github.com/LichtargeLab/BioVNN.

Supplementary information: Supplementary data are available at Bioinformatics online.

Abstract

Grants and funding