Ioannis Vlachos, Ph.D.
Assistant Professor, Harvard Medical School
Co-Director of the Bioinformatics Program, Cancer Research Institute
Director of Bioinformatics, ncRNA Core, Harvard Initiative for RNA Medicine
Department of Pathology, Beth Israel Deaconess Medical Center
330 Brookline Ave, 519A,
Dana Building,
BIDMC, Boston, MA 02115
Ioannis Vlachos, Ph.D., is an Assistant Professor of Pathology in Harvard Medical School and the Director of the Bioinformatics Unit of the Non-coding RNA Precision Diagnostics and Bioinformatics Core. He is also the co-Director of the Bioinformatics Program at the Cancer Research Institute (CRI) and an Associated Faculty Member of the Harvard Stem Cell Institute.
Dr. Vlachos has continuously worked at the forefront of computational non-coding genome research, with a specific focus on non-coding RNAs such as microRNAs and long non-coding RNAs. The databases, models, and algorithms he has created empower researchers worldwide in decrypting ncRNA biogenesis, as well as in prioritizing ncRNAs as biomarkers or therapeutic targets. The systems and servers he has implemented are used by researchers in more than 55 countries worldwide, in all continents, excepting Antarctica. Many of these tools are considered as reference resources and have been deemed as “Expert Databases” in RNACentral and are official data sources in the Ensembl database. In parallel, he continues his research in Machine Learning and AI, where he recently introduced Super Learning for the first time in a biomedical setting.
His research focuses on the effects of non-coding RNAs and non-coding variation on cancer initiation, progression and treatment, as well as immunosurveillance and immunoediting. Non-coding mutations and RNAs can be utilized as effective therapeutic targets or as biomarkers for diagnosis or patient stratification and management. The crosstalk between in silico, in vitro, and in vivo approaches, as well as between bench and bed-side have been central to Dr. Vlachos’s research. His long-term vision is to create the necessary methods and approaches that will enable the complete incorporation of the regulatory non-coding genome in personalized clinical decision making.