Kulandaisamy Arulsamy

Kulandaisamy Arulsamy is a Research Fellow conducting interdisciplinary research at the intersection of vascular biology and computational science. His work integrates experimental vascular studies with advanced bioinformatics to address mechanisms of atherosclerosis, lymphatic development, and protein dysfunction.

His research focuses on two interconnected domains: (1) Vascular pathophysiology, investigating endothelial-to-mesenchymal transition in atherosclerosis, lymphatic valve development, and foam cell formation via proteins like CD45 and epsins; and (2) Computational biology, developing machine learning tools (e.g., TMB Stab-pred, SCIG) for predicting protein stability, disease-causing mutations, and cell identity from genetic sequences. Recent publications highlight his dual expertise in wet-lab vascular experiments and dry-lab algorithm development.

Analysis of his 15 most recent publications (2023-2025) reveals a strong trend toward methodological innovation in protein analysis (60% of works) combined with vascular disease mechanisms (40%). Key contributions include databases for mutation annotation (CarbDisMut) and predictive models for transmembrane protein stability, demonstrating consistent focus on bridging computational predictions with experimental vascular biology.

No scientific awards were documented in the available information.

While student advising details are unreported, his publication pattern suggests active mentorship in interdisciplinary projects combining molecular biology and computational analysis. No grant information was provided, though his tool-development focus implies likely funding for bioinformatics infrastructure.

His research appears conducted within a collaborative environment spanning vascular biology labs and computational groups, though specific laboratory structures or team compositions were not detailed in the source material.