Ritu Tomer

This paper identifies seven distinct sets of prognostic biomarkers for predicting high-risk SKCM patients using transcriptomics data and machine learning models.

AntiFP2 predicts antifungal proteins from genomic and metagenomic data using an ensemble of ESM2, BLAST, and MERCI, achieving high accuracy through cross-validation and offering a scalable pipeline.

This paper develops models to predict RA-inducing peptides using machine learning, deep learning, and large language models, achieving high AUC scores and implementing them in a web tool.

PEPlife2 is an updated manually curated database with 4,412 peptide half-life entries, including structural data and advanced analysis tools.

AntiCP3 predicts anticancer proteins using evolutionary information and hybrid methods, achieving an AUC of 0.91.

The paper introduces IL4Pred2, a host-specific method for predicting IL-4 inducing peptides in human and mouse, achieving high AUC and MCC scores on independent datasets.

This paper develops a machine learning model and software tool to predict NF-κB inhibitors from chemical libraries, achieving reliable predictions validated against experimental data.

This paper develops computational tools to predict celiac disease-associated epitopes and motifs in proteins, achieving high accuracy using machine learning and motif-based approaches.

This paper develops methods to predict and design T1DM-associated peptides, achieving high accuracy with a hybrid approach and a webserver for peptide prediction.

Nfeature computes a large number of features for DNA and RNA sequences, providing a standalone package and web platform for researchers.