School of Bioinformatics · AI & machine learning
Machine Learning for Biology
Machine learning is now part of most bioinformatics job descriptions, and biological data break many of the assumptions in general ML tutorials. This course teaches applied ML with the evaluation discipline biological data demand.
Course overview
Machine Learning for Biology teaches supervised and unsupervised learning on biological data, from feature engineering for sequences, expression profiles and molecules to neural networks for sequence data in PyTorch.
Biological data break assumptions that general tutorials take for granted: samples are related by homology, patients or batches, classes are imbalanced, and random splits leak information. Validation is therefore the core of the course, following community recommendations such as DOME, and every model is compared with a simple baseline that it has to beat.
Learning outcomes
By the end of the course you will be able to:
- Frame biological questions as machine-learning problems
- Engineer features from sequences, expression data and molecules
- Validate without leakage using grouped and homology-aware splits
- Train neural networks on sequence data with PyTorch
- Explain models and report performance and uncertainty honestly
Skills you will practise
Eligibility and prerequisites
Who can apply
- Students and graduates of B.Sc., M.Sc., B.Tech., M.Tech., B.Pharm., M.Pharm., MBBS, BDS, B.V.Sc. or a PhD programme in the life sciences, biotechnology, bioinformatics, pharmacy, medicine, agriculture, chemistry, statistics or computer science
- Researchers, faculty and industry professionals moving into data-driven biology
- Final-year students may apply before their results are declared
Prerequisites
- Python for Bioinformatics or equivalent
- Basic statistics
Best suited to
- Students with Python experience who want to move into AI for biology
- Bioinformaticians adding machine learning to their toolkit
Module-wise syllabus
6 modules. Each pairs instructor-led sessions with a hands-on lab on public data.
Topics
- Supervised and unsupervised learning
- Bias, variance and baselines
- The scikit-learn workflow
Topics
- k-mers and one-hot encoding
- Expression matrices
- Molecular fingerprints
Topics
- Data leakage in biology
- Grouped, cluster and homology-aware splits
- Metrics for imbalanced data
Topics
- PyTorch basics
- Convolutional networks on DNA
- Protein language-model embeddings
Topics
- Feature importance and SHAP
- Uncertainty and the applicability domain
- Model cards
Topics
- Problem to evaluated model
- Ablations and baselines
- Write-up
Practical sessions and projects
Capstone project
Predict a biological property
Build, validate and explain a model on a public biological dataset, including a baseline it must beat and a model card that states its limits.
Assessed on the correctness of the analysis, the quality of the code and the reasoning in the write-up. A pass is required for the certificate.
- ML foundations
Train logistic-regression and random-forest classifiers with scikit-learn and compare them with a majority-class baseline.
- Biological features
Encode DNA as k-mers and one-hot matrices, expression profiles as normalised features and molecules as fingerprints.
- Validation done right
Show how a random split inflates performance on related sequences, then repeat the evaluation with grouped and homology-aware splits.
- Deep learning
Train a small convolutional network on DNA sequences in PyTorch and compare it with a k-mer model.
- Interpretation
Explain a model with permutation importance and SHAP, estimate its uncertainty and write a model card.
- Project: an end-to-end model
Build, validate and report an end-to-end model on a public dataset, including ablations and the baseline it beats.
Software, tools and datasets
Software and tools
All software is free and open source or free for academic use. A GPU is not required; exercises run on a laptop CPU or a free cloud notebook.
Datasets
- Tox21: Molecular property classification with imbalanced classes.MoleculeNet benchmark collection
- TCGA RNA-seq expression: Classification from expression profiles with patient-grouped splits.NCI Genomic Data Commons
- Promoter sequences: Sequence models: k-mers against convolutional networks.Eukaryotic Promoter Database (EPD)
Core reading
- Greener JG, Kandathil SM, Moffat L, Jones DT (2022). A guide to machine learning for biologists. Nature Reviews Molecular Cell Biology 23:40–55.
- Walsh I et al. (2021). DOME: recommendations for supervised machine learning validation in biology. Nature Methods 18:1122–1127.
- Pedregosa F et al. (2011). Scikit-learn: machine learning in Python. Journal of Machine Learning Research 12:2825–2830.
Duration, format and fees
- Next cohort
- Dates are sent with your admission offer
- Timetable
- Three evening or weekend sessions a week (IST), plus a lab clinic; intensive and weekend-only schedules on request
- Payment
- Secure online payment link with your admission offer (UPI, cards, net banking). Instalments on request for fees above ₹15,000.
- Refunds
- See the refund and cancellation policy.
Instructor
The instructor for each cohort is named in your admission offer and on this page when the cohort is announced. Vedicgen instructors are selected for hands-on research or industry experience in the subject they teach, and every course is reviewed by a second subject expert.
Questions about who teaches a cohort? [email protected]
Certificate
Learners who complete the course and pass its capstone receive a Vedicgen certificate of completion for Machine Learning for Biology, issued by Vivofold, stating the guided hours and skills assessed. Each certificate has a unique ID and QR code that anyone can check.
Apply for Machine Learning for Biology
Apply
Send the form below. It takes about five minutes.
Review
An adviser confirms your fit and format within two working days.
Offer
You receive dates, timetable and a secure payment link.
Onboard
Set-up guide, pre-reading and joining links.
Frequently asked questions
No. Exercises are sized for a laptop CPU or a free cloud notebook.
In the live cohort, modules are taught in instructor-led sessions with a weekly lab clinic where you work on real public data with help at hand; every session is recorded. The self-paced format uses the same lessons, labs and datasets with mentor review of your capstone. Both formats end with an assessed project reviewed for the code and the science.
A laptop with at least 8 GB of RAM and about 30 GB of free disk space, running Linux, macOS, or Windows with WSL2. A GPU is not required. Installation instructions are sent before the first session, and lab clinics help with any set-up problems.
Yes. Learners who complete the course and pass its assessment receive a Vedicgen certificate of completion, issued by Vivofold. Every certificate carries a unique ID and a QR code that anyone, including an employer or admissions office, can check on the certificate verification page.
Apply with the form on this page. Live and in-person cohorts run through the year; the dates, timetable and joining details of the next cohort are sent with your admission offer and listed on this page once announced. Self-paced enrolment opens as soon as your application is accepted.
Yes. No course runs longer than four weeks, and the length is customisable: choose the standard schedule, an intensive one- to two-week schedule, weekends only, or a custom timetable. Pick your preference on the application form; colleges and teams can book their own dates.
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