🎓 Bioinformatics Student | Machine Learning & Genomics Enthusiast
💻 Department of Bioinformatics, Marwadi University
🌱 Exploring computational biology, transcriptomics, and AI-driven drug discovery
- 🔬 Passionate about integrating machine learning with biological data to solve real-world healthcare challenges.
- 🧬 Researching antimicrobial resistance prediction, cancer genomics, and graph-based biological networks.
- ☁️ Hands-on with Python, R, C#, SQL, Azure, and Streamlit.
- 💡 I love designing data-driven bioinformatics pipelines — from raw sequencing to predictive modeling.
- 🎥 Also a creative After Effects video editor (because science should look good too 😉).
Machine learning-based pipeline for predicting antimicrobial resistance in Acinetobacter baumannii using k-mer genomic features.
Used Catboost with TCGA data to predict lung adenocarcinoma treatment outcomes.
Analyzed the PI3K-AKT signaling pathway for breast cancer progression and potential drug targets.
Molecular modeling and therapeutic site prediction using computational structural biology.
Languages:
Python • R • C# • SQL
Machine Learning:
scikit-learn • XGBoost • LightGBM • Pandas • NumPy
Bioinformatics Tools:
Biopython • Scanpy • KEGG API • TCGA data • BV-BRC
Visualization:
Matplotlib • Seaborn •
Others:
GitHub • Azure OpenAI • Streamlit • FAISS • LangChain
- 🧪 Built an ensemble ML model achieving ~80% accuracy in AMR prediction
- 🧠 Applied feature selection & ensemble learning for biological datasets
- 📊 Experienced with omics data analysis, gene expression, and network biology
- 🌐 Currently exploring Graph Neural Networks for antibiotic combination prediction
📧 [email protected]
🔗 LinkedIn
💻 GitHub
⭐ "Bridging biology and machine learning — one dataset at a time."