Dr. Raghvendra is an experienced machine learning researcher with expertise in systems biology, structural biology, complex networks, and data sciences, working on multi-omics data integration to understand disease vagaries, identify therapeutic targets, and gain novel biological insights using data-driven techniques.
Dr. Raghvendra develops bioinformatics tools and methods to leverage the power of big biomedical data to extract meaningful biology. He is a seasoned researcher, with a distinguished publication record of 60+ peer-reviewed articles (1000+ citations, h-index: 19) and an extensive background in machine learning and computational biology. He thrives in a dynamic, interdisciplinary environment, presenting innovative solutions to complex problems in a concise manner to diverse audiences of quantitative, experimental, and clinical scientists.
Qatar Computing Research Institute; Hamad Bin Khalifa University, Qatar2018 - 2020
Qatar Computing Research Insitute; Hamad Bin Khalifa University, Qatar2016 - 2020
KU Leuven; Leuven, Belgium2015
IIIT-H; Hyderabad, India2011
IIIT-H; Hyderabad, India2010
Kolatkar and H. Bensmail; BCrystal: an interpretable sequence- based protein crystallization predictor; Bioinformatics; https://academic.oup.com/bioinformatics/article-abstract/36/5/1429/5585746
Comparison and assessment of family- and population- based genotype imputation methods in large pedigrees; Genome Re- search; doi:10.1101/gr.236315.118
DeepCrystal: A Deep Learning Framework for Sequence-based Protein Crystallization Prediction; Bioinformatics; doi.org/10.1093/bioinformatics/bty953
RGBM: regularized gradient boosting machines for identification of the transcriptional regulators of discrete glioma subtypes; Nucleic Acids Research; doi.org/10.1093/nar/gky015
Deep- Sol: a deep learning framework for sequence-based protein solubility prediction; Bioinformatics; https://doi.org/10.1093/bioinformatics/bty166
A metabolic function of FGFR3-TACC3 gene fusions in cancer; Nature; https://doi.org/10.1038/nature25171