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GeneWalk identifies relevant gene functions for a biological context using network representation learning.

In Genome biology ; h5-index 114.0

A bottleneck in high-throughput functional genomics experiments is identifying the most important genes and their relevant functions from a list of gene hits. Gene Ontology (GO) enrichment methods provide insight at the gene set level. Here, we introduce GeneWalk ( github.com/churchmanlab/genewalk ) that identifies individual genes and their relevant functions critical for the experimental setting under examination. After the automatic assembly of an experiment-specific gene regulatory network, GeneWalk uses representation learning to quantify the similarity between vector representations of each gene and its GO annotations, yielding annotation significance scores that reflect the experimental context. By performing gene- and condition-specific functional analysis, GeneWalk converts a list of genes into data-driven hypotheses.

Ietswaart Robert, Gyori Benjamin M, Bachman John A, Sorger Peter K, Churchman L Stirling

2021-Feb-02

Differential expression, Functional analysis, GO enrichment, Gene set enrichment analysis, GeneWalk, INDRA (Integrated Network and Dynamical Reasoning Assembler), Machine learning, NET-seq, Network representation learning, Next-generation sequencing, Pathway Commons, RNA-seq