📅 Day 1: AI-Driven Scientific Data Analysis and Computational Modelling
- Convergence of AI, biotechnology, and nanotechnology in advanced research
- Scientific datasets and data-driven research problem formulation
- Basics of computational thinking for interdisciplinary research
- Machine learning workflow for scientific applications
- Data preprocessing, visualization, model building, and result interpretation
- Applications of AI in healthcare, bioinformatics, nanomaterials, agriculture, energy, and sustainability
🛠️ Hands-on:
- Setting up a Google Colab-based research workflow
- Importing and exploring a sample scientific dataset
- Performing data cleaning and preprocessing
- Visualizing research data using Python
- Building a basic ML model for prediction/classification
- Interpreting model output for scientific decision-making
🧰 Tools Covered: Google Colab, Python, NumPy, Pandas, Matplotlib, Scikit-learn
🎯 Practical Output: Participants will build a basic AI-enabled scientific data analysis workflow and understand how ML models can support research interpretation.
🧬 Day 2: Bioinformatics, Molecular Data Mining, and Network-Based Biological Analysis
- Biological data resources for modern biotechnology research
- Gene, protein, sequence, compound, and molecular data interpretation
- Role of AI and computational biology in drug discovery and diagnostics
- Biological database mining for research problem identification
- Protein-protein interaction networks and biomarker-oriented analysis
- Applications in disease research, biomarker discovery, precision medicine, and therapeutic screening
🛠️ Hands-on:
- Retrieving gene/protein information from NCBI and UniProt
- Exploring protein functions and annotations
- Searching compound and molecular properties using PubChem
- Building a protein-protein interaction network using STRING Database
- Visualizing and interpreting a biological network using Cytoscape
- Preparing a short research interpretation from biological and molecular data
🧰 Tools Covered: NCBI, UniProt, PubChem, STRING Database, Cytoscape, Google Colab
🎯 Practical Output: Participants will complete a bioinformatics mini-workflow involving database mining, molecular information analysis, and biological network visualization.
🔬 Day 3: Nanotechnology, Materials Informatics, and Bio-Nano-AI Research Design
- Nanotechnology and nanomaterials for interdisciplinary research
- Nanomaterials in healthcare, biosensors, drug delivery, energy, environment, and smart materials
- Bio-nano interfaces and AI-enabled material discovery
- Materials informatics and property-based data exploration
- Literature mapping and research gap identification
- Concept note development for interdisciplinary research, innovation, publication, or prototype design
- Ethical, biosafety, nanotoxicity, and responsible innovation considerations
🛠️ Hands-on:
- Exploring nanomaterial and materials science data using public platforms
- Reviewing recent research articles from PubMed and Google Scholar
- Identifying a research gap in AI, biotechnology, or nanotechnology
- Designing a bio-nano-AI solution for healthcare, diagnostics, energy, environment, or materials innovation
- Drafting a mini research proposal or innovation concept note
- Presenting a short interdisciplinary project concept
🧰 Tools Covered: Materials Project, NanoHub, PubMed, Google Scholar, Google Colab
🎯 Practical Output: Participants will develop a mini interdisciplinary research concept combining AI, biotechnology, and nanotechnology with a clear problem statement, tools, methodology, and expected outcome.
