Arjun Krishnan

Arjun Krishnan is a Fixed Term Assistant Professor in the Department of Biochemistry & Molecular Biology at Michigan State University, with joint faculty appointments in both the Neuroscience Program and Genetics & Genome Sciences Program. His research is based in the Engineering Building, reflecting his computational and data-driven approach to biomedical research.

Dr. Krishnan's research focuses on developing and applying computational data-driven approaches to resolve, understand, and tackle the heterogeneity of complex traits and diseases. His lab builds genome-scale models and generates systematic predictions by leveraging large genomics and biomedical data collections using statistics and machine learning. His specific interests include unraveling the genetic strata/subtypes of complex traits/diseases, understanding age- and sex-specificity of traits/disease, and identifying cross-species models for human traits/diseases.

• Specializes in computational genomics and biomedical data analysis
• Focuses on precision medicine approaches through genomic profiling
• Develops genome-scale models for disease understanding

Analysis of Dr. Krishnan's publication record shows a strong focus on cancer biology, particularly multiple myeloma, with numerous high-impact publications in journals like Nature, Science Translational Medicine, and the New England Journal of Medicine. His work spans computational biology, molecular mechanisms of disease, and clinical applications of genomic insights, with a recent emphasis on immunotherapy approaches for hematological malignancies.

Dr. Krishnan has contributed to significant clinical guidelines and consensus statements in neurology, particularly regarding idiopathic intracranial hypertension and Bell's palsy, demonstrating the breadth of his research impact across different medical specialties.

His collaborative research approach is evident in his numerous co-authorships across diverse fields, working in synergy with experimental and clinical researchers to motivate methods development and test predictions in human and model systems.