PRESGENE: A web server for PRediction of ESsential GENE using integrative machine learning strategies
Nandi, S.; Panditrao, G.; Ganguli, P.; Sarkar, R. R.
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Study of essential genes in disease-causing organisms has wide application in the prediction of therapeutic targets and exploring different clinical strategies. Predicting gene essentiality for large set of genes in non-model, less explored organisms is challenging. Computational methods that use machine learning (ML)-based strategies are popularly adopted for essential gene prediction as they provide key advantage of considering diverse biological features. Previous works from our group have demonstrated two ML-based pipelines for predicting essential genes with high accuracy that mitigates the problems of sufficient labeled imbalanced dataset and limited labeled datasets of essential genes. Here we present PRESGENE at https://presgene.ncl.res.in, a ML-based web server for prediction of essential genes in unexplored eukaryotic and prokaryotic organisms. Our algorithms mitigate the problems of training dataset imbalance and limited availability of experimentally labeled data for essential genes. PRESGENE with its user-friendly web interface and high accuracy will prove to be a seamless experience for biologists looking for an accurate essential gene prediction server with limited labeled data for novel organisms.
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