Dinesh Kumar Barupal

In-silico Tandem Mass Spectral Libraries for the Blood Exposome and Metabolomics Workbench Databases: Application of Gemini Large Language Model

The paper generates in-silico mass spectral libraries for human metabolomics using the Gemini large language model, achieving 55% accuracy compared to NIST 23.

Predicting Toxicity and Bioactivity of the Chemical Exposome: A Case Study for the Blood Exposome Database

The paper predicts toxicity and bioactivity of chemicals in the Blood Exposome Database using machine learning models trained on Tox21 data, enabling scalable prioritization of chemical exposures.

Enumerating the chemical exposome using in-silico transformation analysis : an example using insecticides

The paper demonstrates an in-silico workflow to enumerate chemical transformation products of insecticides using machine learning and chemical data resources, identifying 19,392 unique products.

Simulating Tandem Mass Spectra for Small Molecules using a General-Purpose Large-Language Model

The paper uses a large-language model to simulate tandem mass spectra for small molecules, bypassing the need for domain-specific training.

IDSL.GOA: Gene Ontology Analysis for Interpreting Metabolomic datasets

The paper introduces IDSL.GOA, a tool for Gene Ontology analysis of metabolomic datasets, highlighting its ability to identify overrepresented GO terms not covered by existing pathway databases.

IDSL.CSA: Composite Spectra Analysis for Chemical Annotation of Untargeted Metabolomics Datasets

IDSL.CSA generates composite mass spectra libraries from MS1-only data to annotate untargeted metabolomics datasets without MS2 fragmentation spectra, achieving comparable results to MS/MS libraries in human blood samples.

IDSL.UFA assigns high confidence molecular formula annotations for untargeted LC/HRMS datasets in metabolomics and exposomics

IDSL.UFA annotates molecular formulas for untargeted LC/HRMS data, achieving high accuracy in metabolomics and exposomics studies.

IDSL.CCDB: a database for exploring inter-chemical correlations in metabolomics and exposomics datasets

The paper introduces IDSL.CCDB, a database that catalogs inter-chemical correlations in metabolomics and exposomics datasets, enabling exploration of chemical relationships in human studies.

Leveraging a founder population to identify novel rare-population genetic determinants of lipidome

The paper identifies novel genetic determinants of lipidome variation in a founder population, highlighting rare alleles associated with specific lipid species and replicating known common loci.

Algorithmic Learning for Auto-deconvolution of GC-MS Data to Enable Molecular Networking within GNPS.

The paper develops a machine learning workflow for auto-deconvolution of GC-MS data within GNPS, enabling molecular networking and applications in breathomics and volatilome mapping.

Prioritization of metabolic genes as novel therapeutic targets in estrogen-receptor negative breast tumors using multi-omics data and text mining

The paper identifies under-reported metabolic genes as potential drug targets in ERneg breast tumors using multi-omics data and text mining.

A comprehensive plasma metabolomics dataset for a cohort of mouse knockouts within the international mouse phenotyping consortium

The paper provides a metabolomics dataset from 30 mouse gene knockouts and wildtypes, identifying 832 metabolites across multiple analytical methods.

Serum triglycerides in Alzheimer’s disease: Relation to neuroimaging and CSF biomarkers

The paper investigates the association between triglyceride principal component scores and Alzheimer's disease biomarkers in the ADNI cohort, finding significant links with MCI, AD, and specific brain structures.

Sets of Co-regulated Serum Lipids are Associated with Alzheimer Disease Pathophysiology

The paper identifies lipid sets in blood associated with Alzheimer's disease biomarkers and clinical symptoms using lipidomic profiling in the ADNI1 cohort.