Bioinformatician · Computational Biologist
Shrivatsa
Hegde
I work at the intersection of molecular biology and computation — identifying drug candidates through docking, tracing gene expression across cancer subtypes, and building tools that make genomic data intelligible.
About
My work is fundamentally about translation — converting raw biological sequences, protein structures, and expression matrices into conclusions that matter for medicine. I have a particular interest in cancer genomics, drug discovery, and NGS analysis, and I enjoy the entire arc from data wrangling to publication.
I'm fluent in R, Python, Bash, and C, and comfortable in HPC environments. Beyond research, I build interactive tools in R Shiny that make bioinformatics analyses accessible without a command line.
I'm always open to collaboration on drug discovery, machine learning in biology, research publications, and open science initiatives.
Experience
Investigated molecular docking, homology modelling, and gene expression analysis. Published on the computational approach to studying phytochemicals against cancer targets. Conducted differential gene expression studies, built expression matrices, and identified DEGs across cancer subtypes.
View Publication →Applied BioNLP techniques to identify and analyse biomedical named entities. Built foundational skills in machine learning and its application within bioinformatics research workflows.
Drug safety monitoring, adverse event reporting, signal detection, and risk assessment. Gained exposure to global regulatory frameworks for post-market drug surveillance.
Education
Publications
Targeting FAK, VEGF, and MTA 1 Proteins with Terminalia elliptica: A Computational Approach for Anticancer Activity
Read Paper →Application of Microbial Technique for the Synthesis of Organic Acid from Different Agrobiomasses
Read Chapter →Projects
Comprehensive computational analysis of PTK2 protein expression across breast cancer subtypes. Identified significant correlations between PTK2 and tumour stemness, proliferation scores, and EMT. Deployed gene expression profiling, pathway enrichment, and statistical modelling in R and Python.
Virtual screening and molecular docking of phytochemicals from Terminalia elliptica against FAK. Identified chebulagic acid as a lead molecule with superior binding affinity compared to the reference drug podophyllotoxin. Published in Frontiers in Oncology.
Certifications
Modern AI systems work model and how to respond effectively to their outputs.
View Certificate →Key AI principles, frameworks, and practical applications.
View Certificate →Linux essentials, shell scripting, and R-based analysis and visualisation pipelines.
View Certificate →Advanced expertise in NGS technology and complex genomic data analysis.
View Certificate →Python data analysis skills — NumPy, Pandas, and data visualisation.
View Certificate →Comprehensive Linux administration — system management, shell, and networking.
View Certificate →Galaxy-based NGS workflows, genetics fundamentals, and bioinformatics pipelines.
View Certificate →Molecular docking and MD simulation — essential for drug discovery workflows.
View Certificate →Interactive Bio Tools
Pairwise Sequence Alignment
Needleman-Wunsch global alignment. Enter single-letter amino acid codes (max 300 aa each).
Needleman-Wunsch · BLOSUM-inspired scoring · runs in-browser
Ramachandran Plot
Visualise backbone dihedral angle distributions. Upload a PDB file or use the demo to see α-helix, β-sheet, and left-handed clusters.
Demo uses simulated data · full PDB analysis via Shiny App
The complete interactive toolkit — Proteomics Analyzer, DGE Analysis, Volcano Plot Builder, Protein Properties, and more — lives in the full R Shiny application.
Open Shiny App →