Director, Bioinformatics and Metabolomics Unit, The Center for Immunometabolism

Highlights of my experiences: •AI expertise in predictive analysis, debunking disease heterogeneity, and dissecting disease pathophysiology. •Expertise in bioinformatics and 'omics data analysis, encompassing single-cell RNA sequencing, bulk RNA sequencing, proteomics, and metabolomics, alongside a strong foundation in exploratory systems biology. •Expertise in analyzing clinical and human cohort data using health informatics. •Expertise in human cohort designs. •Competence with genetic mouse models. •Specialization in metabolism, pancreatic beta-cell biology, and atherosclerosis. •Knowledge of immunology, lysosome, and autophagy mechanisms. •Background in drug formulation, pharmacology, and toxicological studies. My discoveries are: • Understanding disease pathophysiology ➢tuberculosis and host immune system interaction ➢early-stage diabetes pathophysiology, ➢immune cell heterogeneity in atherosclerosis progression. •Drug discovery (i.e., discovered the pivotal roles of CERS2 in type 2 diabetes), •Prognostic/diagnostic/biomarker discovery (i.e., discovered several predictive biomarkers of type 2 diabetes). ➢In the first model, optimized using a decision tree-based ML approach, we identified a seven-lipid metabolite T2D predictive signature with a discrimination power (AUC) of 0.92 (87% sensitivity, 93% specificity, and 91% accuracy). ➢In the second model, which combines the random forest ranking algorithm with a univariate ranking system, followed by the application of the sMLR algorithm, we identified a three-analyte predictive risk-score panel with a discrimination power (AUC) of 0.95 (86% sensitivity, 90% specificity, and 88% accuracy). •Pharmacogenomics (i.e., discovered comprehensive mode of action of isoniazid in tuberculosis) & toxicogenomic. •Bioinformatics tool design: metGWAS 1.0. •AI-assisted drug design platform. •Designed more than 70 drug formulations. C. Highlighted Manuscripts a. Saifur R. Khan* (2025); Reduced circulating sphingolipids and CERS2 activity are linked to T2D risk and impaired insulin secretion. Science Advances, Volume 11, Issue 2, eadr1725, PMID: 39792658 (*corresponding author). b. Saifur R Khan* (2025), Early Postpartum Metabolic Heterogeneity Among Women Who Progressed to Type 2 Diabetes After Gestational Diabetes: A Prospective Cohort. Diabetes/Metabolism Research & Reviews, Volume 41, Issue 1, e70027, PMID: 39810724 (*corresponding author). c. Saifur R. Khan (2019); The discovery of novel predictive biomarkers and early-stage pathophysiology for the transition from gestational diabetes mellitus to type 2 diabetes mellitus. Diabetologia, Volume 62, Issue 4, pp 687–703, PMID: 30645667, PMCID: PMC7237273. d. Saifur R. Khan (2020); Diminished sphingolipid metabolism, a hallmark of future type 2 diabetes pathogenesis, is linked to pancreatic beta-cell dysfunction. iScience, Vol. 23, Issue 10, PMID: 33103069, PMCID: PMC7578680. e. Saifur R. Khan* (2019); Unbiased data analytic strategies to improve biomarker discovery in precision medicine. Drug Discovery Today, Volume 24, Issue 9, Pages 1735-1748, PMID: 31158511 (*corresponding authors). f. Saifur R. Khan* (2023); metGWAS 1.0: Network driven over-representation analysis between independent metabolomic and meta-genome wide association studies identifies gene-metabolite relationships to phenotype. Bioinformatics 2023 Sep 2;39(9), PMID: 37610350 PMCID: PMC10491949 (*corresponding author). g. Saifur R. Khan* (2021); Integration of AI and traditional medicine in drug discovery; Drug Discovery Today. h. Saifur R. Khan* (2019); Isoniazid and host immune system interactions: a proposal for a novel comprehensive mode of action. British Journal of Pharmacology, 2019 Dec; 176(24): 4599–4608. PMCID: PMC6965675, PMID: 31517993 (*corresponding author).