The scale of genetic, epigenomic, transcriptomic, cheminformatic and proteomic data
available today, coupled with easy-to-use machine learning (ML) toolkits, has propelled the …

Biological networks are powerful resources for the discovery of genes and genetic modules
that drive disease. Fundamental to network analysis is the concept that genes underlying the …

The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …

The rapid increase in the number of proteins in sequence databases and the diversity of
their functions challenge computational approaches for automated function prediction. Here …

The field of machine learning, which aims to develop computer algorithms that improve with
experience, holds promise to enable computers to assist humans in the analysis of large …

Spurred by advances in processing power, memory, storage, and an unprecedented wealth
of data, computers are being asked to tackle increasingly complex learning tasks, often with …

Abstract GeneMANIA (http://www. genemania. org) is a flexible, user-friendly web interface
for generating hypotheses about gene function, analyzing gene lists and prioritizing genes …

Single-cell RNA-sequencing (scRNA-seq) technology provides a new avenue to discover
and characterize cell types; however, the experiment-specific technical biases and analytic …

Protein-RNA interactions are fundamental to core biological processes, such as mRNA
splicing, localization, degradation, and translation. We developed a photoreactive …

The GeneMANIA Cytoscape plugin brings fast gene function prediction capabilities to the
desktop. GeneMANIA identifies the most related genes to a query gene set using a guilt-by …