Deep learning

Mutual information

Pattern recognition (psychology)

Genomics

Inference

Biology

Gene

Data mining

Machine learning

Gene expression

Encoding (memory)

Functional genomics

Genome

Artificial intelligence

Graphical model

Computer science

Gene regulatory network

Computational biology

Cluster analysis

Genetics

Convolutional neural network

Proceedings of the National Academy of Sciences

Several methods were developed to mine gene-gene relationships from expression data. Examples include correlation and mutual information methods for coexpression analysis, clustering and undirected graphical models for functional assignments, and directed graphical models for pathway reconstruction. Using an encoding for gene expression data, followed by deep neural networks analysis, we present a framework that can successfully address all of these diverse tasks. We show that our method, convolutional neural network for coexpression (CNNC), improves upon prior methods in tasks ranging from predicting transcription factor targets to identifying disease-related genes to causality inference. CNNC's encoding provides insights about some of the decisions it makes and their biological basis. CNNC is flexible and can easily be extended to integrate additional types of genomics data, leading to further improvements in its performance.

Deep learning for inferring gene relationships from single-cell expression data