Pushpendra Singh Gahlot

The paper develops EdiProPred, a model using large language models to classify plant proteins as edible or non-edible based on sequence data, achieving high accuracy.

AptViralDB is a manually curated database of 1,768 experimentally validated antiviral aptamers against 40 viruses and 104 targets, offering annotations, predicted structures, and search tools.

The paper develops PCPpred, a model predicting peptide permeability across assays, achieving high performance on Caco-2 and PAMPA tests using molecular features and ensemble methods.

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.

The paper develops a hybrid model combining alignment-based and alignment-free methods to predict plant resistance proteins, achieving high AUROC scores and creating an online platform for this purpose.