Jayadev Joshi

This paper introduces muODE, a framework that simulates microbial community dynamics using metagenomics data to predict FMT outcomes and C. difficile resistance.

GalaxyMemMD provides a reproducible, high-throughput pipeline for membrane protein simulation and analysis, integrating embedding, MD simulation, and downstream analysis.

This paper uses Hyperdimensional Computing to predict chemical compound toxicity efficiently, showing it can classify compounds as toxic or non-toxic with computational advantages over traditional methods.

The paper introduces a Vector-Symbolic Architecture for encoding viral pangenomes and evaluates its effectiveness in species classification, finding that a flat species-level model outperforms genus-level and hierarchical approaches.

The paper uses forward and reverse degradomics to analyze proteolytic landscapes in aortic aneurysms, identifying key proteases like MMP9 and CMA1 and their roles in extracellular matrix remodeling.

The paper identifies M1 muscarinic and H3 histamine receptors as targets for OPC differentiation and analyzes their ligand interactions using computational methods.

R2G2 automates the creation of Galaxy wrappers for R/Bioconductor packages, simplifying integration and reducing complexity for non-programmers.

This paper compares classical machine learning and a brain-inspired hyperdimensional computing approach for classifying metagenomic samples, showing HDC achieves comparable or better accuracy and efficiency.

The paper introduces g2nb, a notebook environment integrating bioinformatics platforms into a unified interface for genomics analysis.

This study shows that mosGILT in Anopheles gambiae affects immune genes and zpg expression, linking it to ovarian development and Plasmodium infection.

PDAUG provides a Galaxy-based toolset for peptide library analysis, visualization, and machine learning modeling, demonstrating its utility in predicting anticancer peptide properties.

The paper describes integrating refgenie into Galaxy to manage and share reference datasets, with tools for retrieval and a global repository for easy access.