Knowledge-primed neural networks enable biologically interpretable deep learning on single-cell sequencing data.

ABSTRACT:

Background

Deep learning has emerged as a versatile approach for predicting complex biological phenomena. However, its utility for biological discovery has so far been limited, given that generic deep neural networks provide little insight into the biological mechanisms that underlie a successful prediction. Here we demonstrate deep learning on biological networks, where every node has a molecular equivalent, such as a protein or gene, and every edge has a mechanistic interpretation, such as a regulatory interaction along a signaling pathway.

Results

With knowledge-primed neural networks (KPNNs), we exploit the ability of deep learning algorithms to assign meaningful weights in multi-layered networks, resulting in a widely applicable approach for interpretable deep learning.

SUBMITTER: Fortelny N 

PROVIDER: S-EPMC7397672 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature