Srujan Shetty – Unlocking the Future of Biotechnology with Machine Learning Mastery

Srujan Shetty

Introduction

Highly skilled and motivated biotechnology professional with a strong background in Python, machine learning, and the development of predictive models. Experienced in web-based prediction systems and drug discovery applications, with expertise in molecular docking and dynamics.

Projects

• Developed a Predictive Model for HMG-CoA Reductase Bioactivity, achieving 86% accuracy with a Random Forest regression model.
• Web-Based Prediction of Protein-Ligand Docking Scores across multiple organs using Graph Neural Network models.
• Retrieved and filtered high-quality reads from colorectal cancer sequencing data, identifying key mutations and potential molecular markers.
• Conducted in-silico computational analysis of cancer exome datasets to identify molecular markers.

Education

• BE in Biotechnology from RV College of Engineering, CGPA: [hidden]
• 12th Grade: [hidden], 10th Grade: [hidden]

Technical Skills & Interests

• Python
• Machine Learning
• MS Office
• Development of Predictive Models
• Molecular Docking
• Molecular Dynamics

Extra Curricular Activities

• Maintenance Head in Team Krushi
• Participated in regional level Kabbadi
• Volunteering in NSS

Publications & Conferences

• Publication: “Understanding Insulin Mechanisms, Economic Implications, and Future Prospects”. DOI: