Anjali Lathwal

The paper develops a tool to predict, design, and scan IL-2-inducing peptides using machine learning, deep learning, and ensemble methods, achieving high accuracy on multiple datasets.

The paper introduces a software package that computes over 50,000 features for protein/peptide function prediction, including residue-level annotations.

The paper identifies prognostic alternative splicing events and mutations in pancreatic adenocarcinoma, showing improved risk prediction compared to existing models.

The paper develops a prognostic model using multi-omics data to stratify HCC patients into recurrence risk groups, identifying key RNA, miRNA, and methylation features with significant predictive performance.

OvirusTdb is a manually curated repository of oncolytic viruses used in cancer treatment, providing comprehensive information on 5927 virus records and their therapeutic applications.

This paper develops models to predict risk scores for colorectal cancer patients using protein concentrations in the mitochondrial apoptotic pathway, achieving higher accuracy than existing tools.