📅 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.
