Yojana Gadiya

TIMS-Bench evaluates three TIMS feature-finding tools using public and benchmark datasets, identifying strengths and weaknesses in feature detection and annotation.

The paper develops a machine learning model to predict drug-induced phospholipidosis using a large dataset of repurposed drugs tested across multiple cell lines.

The paper develops ML models to predict antimicrobial activity of small molecules using a knowledge graph, achieving 75.9% accuracy in identifying active compounds.

The paper estimates the size of plant chemical space using metabolomics and literature data, suggesting it may span millions of unique compounds, most of which remain unexplored.

The paper develops two annotation pipelines and dashboards to integrate chemical and bioactivity data for drug repurposing, validated on a compound library with over 5000 compounds.

This paper describes a journey towards implementing FAIR data practices in antimicrobial resistance research, transitioning from spreadsheet-based data to a knowledge graph framework.

The paper analyzes patent data from SureChEMBL to assess the drug-likeness and clinical development stage of compounds, providing insights into their relevance for pharmaceutical discovery.

PEMT extracts patent information linked to chemical structures and gene names from public databases to support drug discovery and research.

This paper constructs a Monkeypox knowledge graph to represent chemical entities and biological aspects of MPXV, integrating data for comprehensive analysis.

RPath prioritizes drugs for diseases by analyzing causal paths in knowledge graphs using drug-perturbed and disease-specific transcriptomic signatures, outperforming existing methods.

DecoPath is a web tool that helps interpret pathway enrichment analysis results by comparing across databases and methods, highlighting consensus and discrepancies.

The paper presents a drug-target-mechanism graph to identify synergistic drug combinations for SARS-CoV-2 treatment, validated through experimental testing.

The paper constructs a cause-and-effect knowledge graph of COVID-19 pathophysiology from scientific literature and provides it as a public resource.

The paper investigates the potential link between heme levels and COVID-19 pathogenesis by analyzing protein interactions and common pathways.