Emerging Frontiers in AI, Biotechnology, and Nanotechnology

To provide hands-on exposure to the convergence of AI, Biotechnology, and Nanotechnology, enabling participants to understand emerging research trends, explore practical tools, and develop interdisciplinary research ideas for future scientific innovation.

By attending this workshop, participants will learn to:

• To introduce participants to the interdisciplinary connection between AI, Biotechnology, and Nanotechnology.
• To provide hands-on exposure to tools such as Google Colab, Python, NCBI, UniProt, PubChem, STRING, Cytoscape, and Materials Project.
• To explore practical applications in healthcare, drug discovery, diagnostics, biosensors, smart materials, agriculture, energy, and sustainability.
• To help participants understand how AI can support biological data analysis, nanomaterial research, and scientific innovation.
• To enable participants to develop a basic interdisciplinary research idea or mini project concept.

📅 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.

Participants will be able to:

• Apply AI tools for scientific data analysis and computational modelling
• Explore biological and molecular databases for research applications
• Analyze protein interaction networks using bioinformatics tools
• Understand nanotechnology applications in healthcare, diagnostics, energy, environment, and materials research
• Identify interdisciplinary research gaps using literature and database resources
• Develop a structured bio-nano-AI research concept
• Connect hands-on learning with research publications, innovation projects, prototypes, and future academic work