New Year Offer End Date: 30th April 2024
Program
AI Scientist: Engineering the Future of Computational Biology & Medicine
Build the next generation of AI-driven biomedical intelligence — from genomic data to predictive models and real-time scientific applications.
Skills you will gain:
About Program:
This 3-day workshop introduces participants to the emerging role of an AI Scientist in computational biology and medicine. The workshop focuses on how mathematical modeling, biological data engineering, machine learning, transformer-based sequence models, and interactive AI applications are used to analyze genomic, protein, and biomedical datasets. Through short lectures and guided hands-on activities, participants will understand how AI can support disease risk prediction, biological data analysis, rare anomaly detection, drug-target workflows, and real-time biomedical dashboards.
Aim: To equip students, researchers, academicians, and professionals with foundational and applied skills in AI-driven computational biology and medicine, including biological data handling, mathematical modeling, large-scale data workflows, sequence-based AI models, and biomedical AI application development.
Program Objectives:
- Understand how AI, statistics, and computational biology are connected in modern biomedical research.
- Learn how probability, likelihood estimation, conjugate priors, SVD, and eigenvalues support biological data interpretation.
- Access, clean, and prepare genomic and protein datasets from public biological databases such as NCBI and EBI.
- Explore large-scale biomedical data engineering using tools such as Apache Arrow, Polars, and Dask.
- Understand how Python and R can be combined for machine learning and statistical analysis workflows.
- Learn how transformer-based models are used for DNA, RNA, protein, and biomedical sequence analysis.
- Understand model fine-tuning, rare disease detection, imbalanced data handling, and Focal Loss.
- Build awareness of AI ethics, data privacy, bias, and future trends in autonomous biomedical research.
- Develop practical exposure to biomedical AI pipelines, dashboards, and real-time prediction systems.
What you will learn?
“`html
📅 Day 1: Foundational Data Setup
- Focus: Building the core math foundations and setting up biological datasets.
- Learning how to use conjugate priors and likelihood estimation to predict disease risks and variant changes.
- Using SVD and eigenvalues to reduce high-dimensional biological data into simple, visual groups.
- Writing custom, fast mathematical code using hardware acceleration to understand how AI models learn across a 3D loss surface.
- Accessing, downloading, and cleaning raw genomic and protein data from online databases such as NCBI and EBI.
🛠️ Hands-on:
- Write a Python script using PyMC to calculate disease risk probabilities with clear uncertainty boundaries.
- Build a basic SVD matrix system from scratch using NumPy to sort and group complex genetic data.
🧰 Tools Covered: Python, PyMC, NumPy, NCBI, EBI, Google Colab
📅 Day 2: Data Engineering & Large-Scale Workflows
- Focus: Managing massive datasets and linking different coding tools without crashing your computer’s memory.
- Using Apache Arrow to read, filter, and clean huge data files without overloading your computer’s RAM.
- Setting up a workflow where Python handles the machine learning code and R handles the deep statistical analysis in the same environment.
- Handling heavy genomic datasets by splitting the work across distributed computing networks.
- Designing workflow paths to match candidate drugs with specific biological targets.
🛠️ Hands-on:
- Use Polars and Dask to run sorting and aggregation filters over a large-scale public health dataset.
- Run speed and memory benchmarks to find the fastest way to join two large biological data tables.
🧰 Tools Covered: Python, R, Apache Arrow, Polars, Dask, Google Colab
📅 Day 3: Sequence Models, Automation & Applications
- Focus: Fine-tuning transformer models, handling rare data, and deploying visual AI applications.
- Transformers for Biology: Understanding how deep learning architectures read DNA and protein sequences like sentences, including tools such as AlphaFold.
- Model Fine-Tuning: Customizing pre-trained language and sequence models on small, specific biological datasets using PyTorch Lightning.
- Managing Unbalanced Data: Using specialized loss functions such as Focal Loss to train AI models for rare diseases and anomaly detection.
- Live Dashboards: Creating interactive web apps that display model predictions, data trends, and feature importance in real time.
- Ethics & Future Trends: Discussing AI bias in health metrics, data privacy, and how autonomous laboratories generate new scientific hypotheses.
🛠️ Hands-on:
- Deploy an automated AI pipeline designed to flag rare anomalies in highly imbalanced medical datasets.
- Build a live-updating web app using Plotly Dash that displays automated predictions and structural metrics instantly.
🧰 Tools Covered: Python, PyTorch Lightning, Transformers, Focal Loss, Plotly Dash, AlphaFold, Google Colab
Mentor Profile
Fee Plan
INR 1999 /- OR USD 50
Get an e-Certificate of Participation!
Intended For :
- Graduate students pursuing B.Sc., B.Tech, B.E., B.Pharm, MBBS, BDS, or related degrees
- Postgraduate students pursuing M.Sc., M.Tech, M.E., M.Pharm, MPH, MBA Healthcare, or related programs
- PhD scholars and research fellows working in biology, biotechnology, bioinformatics, computational biology, AI, ML, data science, healthcare, medicine, pharmacy, or engineering
- Academicians, faculty members, trainers, and educators interested in AI applications in computational biology and medicine
- Industry professionals from biotechnology, pharmaceutical, healthcare, diagnostics, biomedical devices, medical AI, data science, and drug discovery sectors
- Early-career researchers and professionals aiming to build skills in biomedical AI, genomics data science, and computational medicine
Career Supporting Skills
Python Programming for Biomedical AI Biological Data Handling Bioinformatics Data Analysis Probabilistic Modeling Dimensionality Reduction
Program Outcomes
- Explain how AI is transforming computational biology, genomics, drug discovery, and medicine.
- Apply basic probabilistic modeling concepts to biomedical risk prediction problems.
- Use dimensionality reduction techniques to simplify and visualize high-dimensional biological data.
- Access and clean biological datasets from public repositories.
- Understand scalable data engineering workflows for large genomic and health datasets.
- Compare memory and speed performance across different data processing tools.
- Understand how transformer models are applied to DNA, RNA, protein, and biomedical sequence data.
- Build basic AI workflows for rare disease detection and anomaly identification.
- Create simple interactive dashboards for biomedical AI predictions.
- Recognize ethical, privacy, and bias-related challenges in AI-powered healthcare research.
- Identify future research and career opportunities in biomedical AI and computational biology.