Essential gene prediction models built so far are heavily reliant on sequence-based features, and the scope of network-based features has been narrow. Previous work from our group demonstrated the importance of using network-based features for predicting essential genes with high accuracy. Here, we apply our approach for the prediction of essential genes to organisms from the STRING database and host the results in a standalone website. Our database, NetGenes, contains essential gene predictions for 2,700+ bacteria predicted using features derived from STRING protein–protein functional association networks. Housing a total of over 2.1 million genes, NetGenes offers various features like essentiality scores, annotations, and feature vectors for each gene. NetGenes database is available from https://rbc-dsai-iitm.github.io/NetGenes/.
Original Paper: [bibtex file=karthikraman-publications.bib key=Senthamizhan2021NetGenes]
Blog Article: Predicting Essential Genes through Network Approach: Deciphering basis of Life (on RBCDSAI Blog)
Access the NetGenes database at https://rbc-dsai-iitm.github.io/NetGenes/
