0000-0002-9307-5428 About
Dr. Ramakrishnan Veerabathiran is a distinguished professor and researcher who specializes in genetics and life sciences. His study is also crucial in enhancing our understanding of karyotyping and the genetic variations associated with various metabolic diseases. His research findings provide essential information that can be directly applied to clinical diagnoses and public health efforts, thereby bridging the gap between laboratory science and patient care. As a prolific scholar, Dr. Veerabathiran has made essential contributions to the scientific community by authoring and co-authoring several high-impact research publications and authoritative book chapters. His deep commitment to both teaching and exploratory research continues to inspire, guide, and motivate the next generation of students and researchers in the discipline.
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Publications (171)
- Association of ERBB4 and SHBG gene polymorphisms with polycystic ovarian syndrome in South Indian women: a case–control genetic analysis Save
- Genetic determinants of hypertension: A meta-analysis of AGTR1 and CYP11B2 gene polymorphisms Save
- Association of PCSK9 and CCL22 gene polymorphisms with myocardial infarction in a South Indian population Save
- Genetic variants of APOE and CCR4 as determinants of myocardial infarction among type 2 diabetes patients: a South Indian case–control analysis Save
- Oncogenic Signalling and Immune Checkpoint Crosstalk in Cancer: Emerging Targets and Precision Therapeutic Strategies Save
- Genetic and systems-level regulation of cancer metabolism: From metabolic reprogramming to AI-driven precision oncology Save
- Genetic Risk Factors in Autoimmune Thyroid Diseases: An Umbrella Review of Meta-Analyses and Evidence Credibility Save
- Engineering strategies and translational progress in targeted nanoparticle drug delivery Save
- Genetic and statistical analysis of ESR1 variants in endometriosis-related infertility: a case–control study from South India Save
- Systems immunology meets clinical translation: Multi-omic approaches to predict therapy response in cancer and autoimmune disease Save