GenePlexus: a web-server for gene discovery using network-based machine learning

• Published in: Nucleic acids research Vol. 50; no. W1; pp. W358 - W366
• Main Authors: Mancuso, Christopher A, Bills, Patrick S, Krum, Douglas, Newsted, Jacob, Liu, Renming, Krishnan, Arjun
• Format: Journal Article
• Language: English
• Published: England Oxford University Press 05.07.2022
• Subjects: Computers; Genetic Association Studies; Genome; Humans; Internet; Machine Learning; Software; Web Server Issue
• ISSN: 0305-1048; 1362-4962
• DOI: 10.1093/nar/gkac335

Abstract Biomedical researchers take advantage of high-throughput, high-coverage technologies to routinely generate sets of genes of interest across a wide range of biological conditions. Although these technologies have directly shed light on the molecular underpinnings of various biological processes and diseases, the list of genes from any individual experiment is often noisy and incomplete. Additionally, interpreting these lists of genes can be challenging in terms of how they are related to each other and to other genes in the genome. In this work, we present GenePlexus (https://www.geneplexus.net/), a web-server that allows a researcher to utilize a powerful, network-based machine learning method to gain insights into their gene set of interest and additional functionally similar genes. Once a user uploads their own set of human genes and chooses between a number of different human network representations, GenePlexus provides predictions of how associated every gene in the network is to the input set. The web-server also provides interpretability through network visualization and comparison to other machine learning models trained on thousands of known process/pathway and disease gene sets. GenePlexus is free and open to all users without the need for registration. Graphical Abstract Graphical Abstract GenePlexus is a cloud-based webserver that enables biomedical researchers to discover novel genes functionally similar to their genes of interest using network-based machine learning.