Neelam Sharma

The paper develops a hybrid model for predicting both linear and conformational B-cell epitopes using machine learning and BLAST, achieving an AUC of 0.83 on an independent dataset.

This paper develops a hybrid model combining motif-based and machine learning approaches to predict exosomal proteins with high accuracy, achieving AUROC of 0.85 on an independent dataset.

This paper develops methods to predict IL-5 inducing peptides using alignment-based and alignment-free approaches, achieving high performance with a hybrid model.

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

The paper organizes a DREAM challenge to benchmark predictive models of single-cell signaling using mass cytometry data from breast cancer cell lines.

This paper develops a machine learning model to predict ceftazidime-resistant bacterial strains from beta-lactamase amino acid sequences, achieving AUROC of 0.80 on training data.

SAPdb is a database of experimentally validated peptide nanostructures formed by self-assembly, providing information on chemical modifications, nanostructure types, and experimental conditions.

AntiCP 2.0 predicts anticancer peptides using machine learning models trained on two datasets, achieving high MCC and AUROC scores on training data.

GPSRdocker provides a docker-based container integrating bioinformatics tools for genomics, proteomics, and systems biology, enabling local or cloud use of computational resources.