Piyush Agrawal

Predicting Prognostic Bidirectional Molecular Signatures Associating Myocardial Infarction and Lung Cancer: An In-Silico Perspective

The paper identifies common molecular signatures between myocardial infarction and lung cancer subtypes, proposes potential therapeutic drugs, and demonstrates predictive models for MI classification.

High Throughput Meta-analysis of Antimicrobial Peptides for Characterizing Class Specific Therapeutic Candidates: An in-silico Approach

The paper performs a meta-analysis of 8.6 million antimicrobial peptides to identify class-specific therapeutic candidates using machine learning and structural analysis.

Characterizing the role of exosomal miRNAs in metastasis

This study identifies exosomal miRNAs that influence the pre-metastatic niche across multiple cancers, showing their association with survival and chemotherapy response.

Network-based approach elucidates critical genes in BRCA subtypes and chemotherapy response in Triple Negative Breast Cancer

This study applies a network-based method to identify key genes in BRCA subtypes and chemotherapy response in TNBC, revealing subtype-specific mediators and potential therapeutic targets.

Predicting gene expression changes upon epigenomic drug treatment

The paper predicts gene expression changes after HDACi treatment using machine learning with pre-treatment transcriptome and epigenomic data, achieving high accuracy in distinguishing up- and downregulated genes.

Computing wide range of protein/peptide features from their sequence and structure

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

AntiCP 2.0: An updated model for predicting anticancer peptides

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

SAMbinder: A web server for predicting SAM binding residues of a protein from its amino acid sequence

SAMbinder predicts SAM binding residues in proteins from amino acid sequences using machine learning, achieving MCC 0.61 and AUROC 0.89 on a realistic dataset.

A repository of web-based bioinformatics resources developed in India

IndiaBioDb is a curated repository of bioinformatics resources developed in India, providing access to 258 databases and 543 resources with search and browsing tools.

GPSRdocker: A Docker-based Resource for Genomics, Proteomics and Systems biology

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

HumCFS: A database of fragile sites in human chromosomes

HumCFS is a manually curated database of human chromosomal fragile sites with annotated genes, miRNAs, and disease associations.

Evaluation of protein-ligand docking methods on peptide-ligand complexes for docking small ligands to peptides

The paper evaluates seven docking methods on peptide-ligand complexes and finds AutoDock performs best with TOP poses, while performance differences are smaller with BEST poses.

Computer-aided prediction of antigen presenting cell modulators for designing peptide-based vaccine adjuvants

The paper develops machine learning models to predict antigen presenting cell modulators (A-cell epitopes) and implements them in a web server for vaccine adjuvant design.

Prediction of residue-residue contacts in CASP12 targets from its predicted tertiary structures

The paper describes a method for predicting residue-residue contacts in CASP12 targets using predicted tertiary structures, achieving high precision in specific categories.