Bioinformatics | Computational Biology | Structural Bioinformatics | Machine Learning in Genomics

Welcome to my GitHub profile.

I am a Bioinformatics graduate with an M.Sc. in Bioinformatics, working at the interface of computational biology, genomics, and data science. My work focuses on applying computational methods, data analysis, and machine learning techniques to address complex biological questions.

Through academic research and project-based work, I have developed experience in structural bioinformatics, genomics data analysis, molecular dynamics simulations, and biological database development. I primarily work in Python, R, and Linux-based environments, building reproducible computational pipelines for analysing biological data.

My broader goal is to contribute to research that advances our understanding of genomic regulation, molecular mechanisms of disease, and computational approaches in life sciences.

Experience in analysing large-scale biological datasets to investigate regulatory mechanisms in genomes, with particular interest in non-coding RNA biology and stress response pathways. Work in this area involves integration of high-throughput sequencing data, literature mining, and structured data organisation for biological interpretation.

Experience in studying protein structure, stability, and functional dynamics using computational approaches. This includes the use of protein structure prediction, structural validation tools, and molecular visualisation platforms to investigate how structural changes influence biological function.

Application of molecular dynamics simulations to analyse structural behaviour of biomolecules over time. This includes performing simulations, trajectory analysis, and structural evaluation using parameters such as:

• Root Mean Square Deviation (RMSD)
• Radius of Gyration (Rg)
• Structural stability and conformational dynamics

These approaches help investigate the structural impact of sequence variations and mutations on protein function.

Interest in applying machine learning approaches to biological datasets, particularly for:

• Genomic Feature Analysis
• Predictive Modelling
• Biomarker Discovery
• High-dimensional Biological Data Processing

This involves building data preprocessing pipelines, feature extraction workflows, and model evaluation frameworks in Python.

Experience in organising large biological datasets into structured resources and designing systems that enable efficient querying and exploration of biological information. This includes integrating heterogeneous datasets and creating research-friendly data access frameworks.

• Python
• R
• SQL / MySQL
• Linux / Bash scripting

• Genomics data analysis
• Sequence analysis
• Structural bioinformatics
• Molecular docking
• Molecular dynamics simulations
• Biological database development

• GROMACS – Molecular dynamics simulations
• PyMOL – Molecular visualisation
• UCSF Chimera – Structural analysis and visualisation
• AutoDock – Molecular docking simulations
• VMD – Molecular dynamics trajectory analysis
• PROCHECK – Protein structure validation

• Machine learning for biological datasets
• High-throughput data processing
• Statistical analysis of biological data
• Data integration and workflow development

1. Kaushik, L., Vivek, A., Arora, S., Hamid, F., Mukherjee, K., Bisht, N., Chaudhary, S., Shukla, J., Nawani, S., & Kumar, S. (2026). The Intersection of AI and Genomics in Health and Disease: Advancements and Applications. Progress in Molecular Biology and Translational Science. https://doi.org/10.1016/bs.pmbts.2026.01.013

2. Vivek, A. T., Bhatia, M., Sahu, N., Kaushik, L., et al. (2026). AraNSdb: A Dedicated Database of Stress-Responsive Non-coding RNAs in Arabidopsis thaliana. 3 Biotech, 16, 90. https://doi.org/10.1007/s13205-026-04708-z

3. Hamid, F., Mukherjee, K., Chaudhary, S., Kaushik, L., & Kumar, S. (2026). PFGPred: A Stack Ensemble Classifier for the Identification of Fusion Genes in Plants. DNA Research, dsag005. https://doi.org/10.1093/dnares/dsag005

My research interests lie in the application of computational approaches to understand biological systems, particularly in:

• Computational genomics
• Non-coding RNA biology
• Structural bioinformatics
• Molecular dynamics simulations
• Machine learning in life sciences
• Computational approaches in precision medicine

This GitHub profile contains projects and code related to:

• Bioinformatics pipelines
• Biological data analysis workflows
• Structural bioinformatics tools
• Machine learning applications in genomics
• Reproducible computational research

If you are interested in collaboration, research discussions, or computational biology projects, feel free to connect.

📧 Email lovekaushik271@gmail.com

⭐ This repository space reflects my ongoing work in computational biology, data-driven life science research, and bioinformatics tool development.