Address | Centre of Biomedical Research (CBMR) Department of Data Sciences, SGPGI Campus, Raebareli Road Lucknow, 226014, Uttar Pradesh, India | ||
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Building | New Building | Room no | 314 |
Lab no: | Intercom: | ||
Email: | yogesh@cbmr.res.in | ||
Url | https://scholar.google.com/citations?user=fydOWQ8AAAAJ&hl=en |
We are harnessing advanced AI algorithms and integrative OMICS approaches (including Single-cell, Transcriptomics, Genomics, and Metagenomics) for in-silico drug discovery and the predictive identification of diagnostic, prognostic, and therapeutic biomarkers, enabling early and precise disease diagnosis and treatment strategies.
I am an Assistant Professor in the Department of Data Science, Centre of Biomedical Research, SGPGIMS, campus, Lucknow India. My Lab is focusing on the following areas.
Leveraging AI algorithms and OMICS analysis for in-silico drug discovery and predictive identification of diagnostic biomarkers for early disease diagnosis.
1. Biological Data Mining: Proficient in extracting meaningful patterns and insights from complex biological datasets, including OMICS data (such as single-cell sequencing, RNA bulk, WGS, WES, proteomics, transcriptomics, and metagenomics).
2. In-Silico Drug Discovery: Skilled in utilizing computational methods and algorithms to identify potential drug candidates, conduct virtual screening, predict drug-target interactions, and assess pharmacokinetic properties.
3. AI and Machine Learning Algorithms for Diagnostic Biomarker Discovery: Experienced in developing and applying AI and ML techniques to analyze biomedical data for the discovery of diagnostic biomarkers. Capable of leveraging advanced algorithms to improve patient care through early disease detection.
4. Tumor Microenvironment and T Cell Plasticity: Specialized in studying the intricate interactions within the tumor microenvironment and the plasticity of T cells. Proficient in predicting responses to immunotherapy and chemotherapy and identifying novel biomarkers for disease diagnosis and treatment prognosis.
5. Mathematical Modeling for Disease Mechanisms: Proficient in mathematical modeling techniques, particularly using machine learning approaches, to gain insights into disease mechanisms. Skilled in building predictive models to understand disease progression and identify potential therapeutic targets.
6. Computational Drug Design and Repurposing: Demonstrated expertise in computational drug design, including structure-based and ligand-based approaches. Experienced in repurposing existing drugs for new indications through computational screening and analysis.
7. In-Silico Pharmacokinetic Studies: Experienced in conducting pharmacokinetic studies using computational models to predict drug absorption, distribution, metabolism, and excretion (ADME) properties. Skilled in optimizing drug dosing regimens and assessing drug safety profiles.
Our research at the Department of Data Sciences, Centre of BioMedical Research, SGPGIMS, Lucknow, focuses on Medical Data Sciences. We specialize in developing and deploying computational methods, algorithms, Machine Learning, and OMICS technologies to enhance our understanding of diseases. Our primary goals include identifying biomarkers for early disease diagnosis, repurposing drugs to improve patient treatment, and ultimately reducing the burden on hospitals. By leveraging these approaches, we aim to advance healthcare by optimizing clinical outcomes and patient care.