Yash Barkale

Bioengineering Student | Aspiring Bioinformatician | Tech Enthusiast
4th-year B.Tech Bioengineering student at MIT School of Bioengineering and Sciences, Pune. Completed a 3-month internship at Oilseed Research Centre, Dr. PDKV, Akola, focusing on soil microbial isolation, biochemical testing, and molecular characterization (PCR, gel electrophoresis). Skilled in bioinformatics tools for sequence analysis and microbial identification. Proficient in software development (Python, SQL), data analysis, and version control using Git. Eager to apply computational techniques and data-driven approaches to bioengineering and bioinformatics challenges. Skilled in both leadership and research, demonstrating analytical thinking and technical proficiency.

B.Tech in Bioengineering  |    Current 8.03 Cgpa     |  June 2025

MIT School Of Bioengineering Sciences and Research, Pune

1.Development of Gelatin-Chitosan Nanoparticles Composite Films for Biodegradable Food Packaging

• Prepared gelatin-chitosan nanoparticles using emulsification and ionic gelation techniques, with glutaraldehyde as a cross-linking agent.
• Characterized nanoparticles and composite films for size, morphology (SEM, TEM), mechanical properties, and biodegradability.
• Fabricated composite films for food packaging, evaluating their compatibility, shelf-life extension, and preservation efficacy through migration studies and microbiological analyses.

2.Genetics-GPT: Integrating Genomics with GPT Models for Enhanced Question Answering in Genetics Research"

• Developed Genetics-GPT, a QA database for genomics using GPT models (including GPT-3 and ChatGPT) to provide accurate answers with reduced AI hallucination, leveraging the model's ability to identify its limitations.
• Trained models on NCBI API using specialized techniques such as gene mention detection, entity linking, and database integration to enhance comprehension and generate biomedical data.
• Conducted experiments using Genetics-GPT to create protein-protein interaction networks and solve biomedical challenges, demonstrating its superiority over typical language models in providing precise, contextually relevant biological insights.
• Applied Transformer-based LLMs to successfully decode long DNA sequences, further advancing biological data analysis

3."Revolutionizing Diabetes Management with Non-Invasive Sweat-Based Glucose Monitoring"

• Developed a wearable biosensor for non-invasive glucose monitoring through sweat analysis.
• Utilized iontophoresis for sweat stimulation and enzymatic reactions on microelectrode arrays for glucose detection.
• Integrated IoT for data storage and personalized diabetes management insights.

• Data Science with Python.
• Artificial Intelligence