About Workshop
The Digital Biologist Paradigm: AI-Driven Discovery in the Life Sciences is a 3-day workshop designed to introduce participants to the emerging role of artificial intelligence as a collaborative partner in biological research. The workshop focuses on how AI-driven systems can support data analysis, multi-omics integration, hypothesis generation, drug discovery, biomarker identification, precision medicine, and biological workflow automation. Participants will gain conceptual and hands-on exposure to biological datasets, AI-assisted tools, and practical discovery pipelines used in modern life science research.
Aim
The aim of this workshop is to help participants understand how AI can transform biological research by enabling faster data interpretation, automated workflow design, multi-omics analysis, and intelligent hypothesis generation for life science discovery.
Workshop Objectives
- To explain the shift from traditional biology to data-driven and AI-assisted discovery.
- To familiarize participants with key biological data types such as genomics, transcriptomics, proteomics, imaging data, and electronic health records.
- To introduce basic machine learning concepts relevant to biologists and life science researchers.
- To demonstrate how AI can support biological data preprocessing, visualization, and interpretation.
- To explain the structure of a digital discovery pipeline, including data acquisition, integration, knowledge extraction, hypothesis generation, validation, and iteration.
- To explore real-world applications such as drug discovery, protein structure prediction, biomarker identification, and precision medicine.
- To introduce AI tools such as scientific LLMs, bioinformatics AI platforms, protein-design tools, single-cell analysis tools, and knowledge graph systems.
- To guide participants in building a basic AI-assisted biological workflow.
- To create awareness about ethical considerations and responsible AI use in biomedical and life science research.
Structure
📅 Day 1: Vision of the Digital Biologist
- Core Objective: Understand the evolution of biology from traditional research approaches to AI-enabled discovery.
- Digital Biologist: evolution from traditional biology to AI-enabled discovery
- Current challenges in biological research: data overload and multi-omics integration
- Hypothesis generation and experimental scalability
- Machine learning fundamentals for biologists
- Biological data types: genomics, transcriptomics, proteomics, imaging, and electronic health records
🛠️ Hands-on:
- Hands-on Lab: Explore biological datasets for AI-driven research applications.
- Hands-on Lab: Perform biological data preprocessing and visualization.
🧰 Tools Covered: Biological datasets, data preprocessing tools, visualization workflows
📅 Day 2: Digital Discovery Pipeline
- Core Objective: Learn how AI-driven discovery pipelines support biological data integration, knowledge extraction, hypothesis generation, and validation.
- Biological data acquisition and data integration
- Knowledge extraction from datasets and scientific literature
- AI-assisted hypothesis generation
- Experimental design, validation, and iterative discovery
- Applications in drug discovery, protein structure prediction, biomarker identification, and precision medicine
🛠️ Hands-on & Case Studies:
- Case Study: AI-driven drug discovery workflow
- Case Study: Protein structure prediction for biological research
- Case Study: Biomarker identification using biological datasets
- Case Study: Precision medicine and personalized biological insights
🧰 Tools Covered: AI discovery pipelines, biological data integration tools, knowledge extraction workflows
📅 Day 3: AI Tools for the Digital Biologist
- Core Objective: Explore AI tools and platforms used to build AI-assisted biological workflows.
- Scientific LLMs for biological research and literature understanding
- Bioinformatics AI platforms for biological data interpretation
- Protein-design tools for structure and function exploration
- Single-cell analysis tools for cellular-level biological insights
- Knowledge graph systems for biological relationship mapping
- Ethical considerations and responsible AI in biomedicine
🛠️ Hands-on:
- Hands-on Lab: Select a biological problem for AI-assisted analysis.
- Hands-on Lab: Analyze a biological dataset using AI tools.
- Hands-on Lab: Apply AI platforms to support biological interpretation.
- Hands-on Lab: Build an AI-assisted biological discovery workflow.
🧰 Tools Covered: Scientific LLMs, bioinformatics AI platforms, protein-design tools, single-cell analysis tools, knowledge graph systems
Application Themes: Cancer genomics, drug repurposing, microbiome analysis, and protein function prediction.
Important Dates
Registration Ends
22 August 2026
IST 4:30 PM
Workshop Dates
22 August 2026
05:30 PM IST
What You Will Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
Outcomes
- Understand the concept of the Digital Biologist and its importance in modern life science research.
- Explain how AI can support biological discovery, data analysis, and research automation.
- Identify different biological data types, including genomics, transcriptomics, proteomics, imaging data, and clinical health records.
- Perform basic exploration, preprocessing, and visualization of biological datasets.
- Understand the structure of an AI-driven digital discovery pipeline.
- Apply AI-assisted thinking to biological problems such as drug discovery, biomarker identification, protein structure prediction, and precision medicine.
- Explore scientific LLMs, bioinformatics AI platforms, protein-design tools, single-cell analysis tools, and knowledge graph systems.
- Build a basic AI-assisted biological workflow for a selected biological problem.
- Understand how AI can help in hypothesis generation and experimental planning.
- Recognize ethical issues related to responsible AI use in biomedical and life science research.
- Gain foundational skills for future learning in AI-driven biotechnology, computational biology, bioinformatics, and digital biology research.
Who Should Attend
- Undergraduate and postgraduate students from biotechnology, bioinformatics, life sciences, biomedical sciences, microbiology, biochemistry, genetics, pharmacy, and related fields
- PhD scholars and research scholars working in biological, biomedical, pharmaceutical, or computational biology research
- Faculty members, academicians, and research mentors interested in AI-enabled life science research
- Bioinformatics professionals and data analysts working with biological datasets
- Biotechnology, pharmaceutical, healthcare, and life science industry professionals
- Beginners with basic knowledge of biology and an interest in AI-driven biological discovery
