
Vikas Pejaver, PhD
About Me
Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health and the Department of Genetics and Genomic Sciences in the Icahn School of Medicine at Mount Sinai. His research focuses on the development and application of machine learning methods to relate genetic variation to molecular function and disease phenotypes, with a particular emphasis on rare variants and diseases. His work utilizes a broad array of machine learning techniques on genomic, protein and electronic health record data sets. Dr. Pejaver has a Bachelor’s degree in Biotechnology from the People’s Education Society (PES) Institute of Technology (now PES University) in Bengaluru, India. After that, he received his Master’s degree in Bioinformatics and doctoral degree in Informatics from the School of Informatics and Computing (now School of Informatics, Computing and Engineering) at Indiana University, Bloomington. Dr. Pejaver then completed his postdoctoral training at the Department of Biomedical Informatics and Medical Education (BIME) and the eScience Institute at the University of Washington (UW), where he received the Moore/Sloan and Washington Research Foundation Innovation in Data Science Postdoctoral Fellowship. He also received a K99/R00 Pathway to Independence Award from the National Library of Medicine at the National Institutes of Health. At UW, he was also awarded the Fred Wolf Mentorship Award for his active roles in training and mentoring students in BIME.
Language
Position
Multi-Disciplinary Training Areas
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS]
About Me
Dr. Vikas Pejaver is an Assistant Professor at the Institute for Genomic Health and the Department of Genetics and Genomic Sciences in the Icahn School of Medicine at Mount Sinai. His research focuses on the development and application of machine learning methods to relate genetic variation to molecular function and disease phenotypes, with a particular emphasis on rare variants and diseases. His work utilizes a broad array of machine learning techniques on genomic, protein and electronic health record data sets. Dr. Pejaver has a Bachelor’s degree in Biotechnology from the People’s Education Society (PES) Institute of Technology (now PES University) in Bengaluru, India. After that, he received his Master’s degree in Bioinformatics and doctoral degree in Informatics from the School of Informatics and Computing (now School of Informatics, Computing and Engineering) at Indiana University, Bloomington. Dr. Pejaver then completed his postdoctoral training at the Department of Biomedical Informatics and Medical Education (BIME) and the eScience Institute at the University of Washington (UW), where he received the Moore/Sloan and Washington Research Foundation Innovation in Data Science Postdoctoral Fellowship. He also received a K99/R00 Pathway to Independence Award from the National Library of Medicine at the National Institutes of Health. At UW, he was also awarded the Fred Wolf Mentorship Award for his active roles in training and mentoring students in BIME.
Language
Position
Multi-Disciplinary Training Areas
Artificial Intelligence and Emerging Technologies in Medicine [AIET], Genetics and Genomic Sciences [GGS]
Publications
Selected Publications
- CAGI6 ID panel challenge: assessment of phenotype and variant predictions in 415 children with neurodevelopmental disorders (NDDs). Maria Cristina Aspromonte, Alessio Del Conte, Shaowen Zhu, Wuwei Tan, Yang Shen, Yexian Zhang, Qi Li, Maggie Haitian Wang, Giulia Babbi, Samuele Bovo, Pier Luigi Martelli, Rita Casadio, Azza Althagafi, Sumyyah Toonsi, Maxat Kulmanov, Robert Hoehndorf, Panagiotis Katsonis, Amanda Williams, Olivier Lichtarge, Su Xian, Wesley Surento, Vikas Pejaver, Sean D. Mooney, Uma Sunderam, Rajgopal Srinivasan, Alessandra Murgia, Damiano Piovesan, Silvio C.E. Tosatto, Emanuela Leonardi. Human Genetics
- Evaluating predictors of kinase activity of STK11 variants identified in primary human non-small cell lung cancers. Yile Chen, Kyoungyeul Lee, Junwoo Woo, Dong Wook Kim, Changwon Keum, Giulia Babbi, Rita Casadio, Pier Luigi Martelli, Castrense Savojardo, Matteo Manfredi, Yang Shen, Yuanfei Sun, Panagiotis Katsonis, Olivier Lichtarge, Vikas Pejaver, David J. Seward, Akash Kamandula, Constantina Bakolitsa, Steven E. Brenner, Predrag Radivojac, Anne O’Donnell-Luria, Sean D. Mooney, Shantanu Jain. Human Genetics
- CAGI, the Critical Assessment of Genome Interpretation, establishes progress and prospects for computational genetic variant interpretation methods. Shantanu Jain, Constantina Bakolitsa, Steven E. Brenner, Predrag Radivojac, John Moult, Susanna Repo, Roger A. Hoskins, Gaia Andreoletti, Daniel Barsky, Ajithavalli Chellapan, Hoyin Chu, Navya Dabbiru, Naveen K. Kollipara, Melissa Ly, Andrew J. Neumann, Lipika R. Pal, Eric Odell, Gaurav Pandey, Robin C. Peters-Petrulewicz, Rajgopal Srinivasan, Stephen F. Yee, Sri Jyothsna Yeleswarapu, Maya Zuhl, Ogun Adebali, Ayoti Patra, Michael A. Beer, Raghavendra Hosur, Jian Peng, Brady M. Bernard, Michael Berry, Shengcheng Dong, Alan P. Boyle, Aashish Adhikari, Jingqi Chen, Zhiqiang Hu, Robert Wang, Yaqiong Wang, Maximilian Miller, Yanran Wang, Yana Bromberg, Paola Turina, Emidio Capriotti, James J. Han, Kivilcim Ozturk, Hannah Carter, Giulia Babbi, Samuele Bovo, Vikas Pejaver, Daniel M. Jordan, Jason R. Bobe. Genome Biology