Ever had a list of gene hits and not known what to do? Maybe GO enrichment analysis left you wondering what your favorite gene is doing in your experiment? Check out GeneWalk, our machine Learning method that determines the relevant functions for each gene.

GeneWalk assembles an experiment-specific gene regulatory network from a knowledge base (e.g. Pathway Commons or INDRA) with genes and GO terms as nodes. neural network-based representation learning converts the gene network nodes into vectors describing the network topology for each node.

Comparison of gene and GO term vectors identifies which genes are highly connected and which GO terms are most relevant for each gene in the experiment. So GeneWalk reveals which genes are central to the specific biological context and it identifies what their roles are.

Further documentation

Citation

Robert Ietswaart, Benjamin M. Gyori, John A. Bachman, Peter K. Sorger, and L. Stirling Churchman
GeneWalk identifies relevant gene functions for a biological context using network representation learning,
Genome Biology 22, 55 (2021). https://doi.org/10.1186/s13059-021-02264-8