Cancer Risk Prediction with Machine Learning for Bioinformatics
Predict Cancer Risk Early Using Machine Learning and Bioinformatics
About This Course
Cancer risk prediction is a critical component of precision medicine, enabling early intervention and personalized screening strategies. Advances in bioinformatics have generated large-scale datasets—from genomics and transcriptomics to clinical and epidemiological records—that can be leveraged to predict cancer susceptibility. Machine learning provides powerful tools to model complex, non-linear relationships within these datasets that traditional statistical approaches often miss.
This workshop introduces ML-driven cancer risk modeling workflows, emphasizing dry-lab, reproducible analysis using Python-based tools. Participants will learn how to preprocess biological and clinical datasets, engineer meaningful features, handle class imbalance, and build predictive models for cancer risk classification. Practical sessions focus on model evaluation, interpretability, and ethical considerations to ensure responsible and clinically relevant predictions.
Aim
This workshop aims to train participants in developing machine learning–based models for cancer risk prediction using bioinformatics data. It focuses on integrating genomic, transcriptomic, clinical, and lifestyle features to identify cancer risk patterns. Participants will learn how predictive models support early detection, stratification, and preventive strategies. The program bridges cancer biology with data-driven intelligence for translational research and precision health.
Workshop Objectives
- Understand cancer risk factors from biological and clinical perspectives.
- Learn preprocessing and feature engineering for cancer-related datasets.
- Build ML models for cancer risk classification and stratification.
- Evaluate models using clinically relevant performance metrics.
- Interpret predictions responsibly with explainable AI approaches.
Workshop Structure
Day 1: Bioinformatics Data Foundations & ML Readiness
- Cancer risk prediction use-cases (screening, stratification, prognosis vs risk)
- Data types & formats: clinical tables, gene expression (RNA-seq/microarray), mutation features, methylation basics
- Python foundations for bioinformatics ML: NumPy, Pandas, Matplotlib; dataset structuring
- Data preprocessing: missing values, encoding, scaling, normalization concepts for omics
- Exploratory data analysis (EDA): distributions, class imbalance, correlations, feature sanity checks
- Feature engineering: pathway scores intro, aggregation strategies, variance filtering
- Tools: Jupyter/Colab, Pandas, NumPy, Matplotlib, Scikit-learn
Day 2: ML Models for Cancer Risk Prediction (Hands-on)
- Supervised learning models: logistic regression, random forest, XGBoost/GBM overview, SVM
- Handling imbalance: class weights, sampling methods (SMOTE concept + safe usage)
- Model validation: train/val/test split, stratification, cross-validation, leakage prevention
- Performance metrics: ROC-AUC, PR-AUC, precision/recall, F1, confusion matrix; thresholding for decision-making
- Interpretability: feature importance, coefficients, permutation importance, SHAP overview
- Case study lab: build a baseline risk classifier from bioinformatics + clinical features
- Tools: Scikit-learn, (optional) XGBoost, Seaborn/Matplotlib, SHAP (intro)
Day 3: Advanced Techniques, Reproducibility & Research-Grade Reporting
- Advanced feature selection: L1 regularization, RFECV, mutual information, stability considerations
- Hyperparameter tuning: GridSearchCV/RandomizedSearchCV, model comparison table
- Pipeline best practices: preprocessing + modeling with Pipeline, reproducible seeds, documentation
- Reporting: figures, metric tables, model cards, limitations, bias/fairness basics for healthcare ML
- Mini-project: end-to-end cancer risk prediction pipeline + final report structure
Optional extension: survival analysis direction (what changes, what to read next)
Tools: Scikit-learn Pipelines, GridSearchCV, Jupyter/Colab
Who Should Enrol?
- Doctoral Scholars & Researchers: PhD candidates seeking to integrate computational workflows into their molecular research.
- Postdoctoral Fellows: Early-career scientists aiming to enhance their data-driven publication profile.
- University Faculty: Professors and HODs interested in modern bioinformatics pedagogy and tool mastery.
- Industry Scientists: R&D professionals from the Biotechnology and Pharmaceutical sectors transitioning to genomic-driven discovery.
- Postgraduate Students: Final-year PG students looking for specialized research-grade exposure beyond standard curricula.
Important Dates
Registration Ends
02/07/2026
IST 07:00 PM
Workshop Dates
02/07/2026 – 02/09/2026
IST 08:00 PM
Workshop Outcomes
Participants will be able to:
- Build and validate ML models for cancer risk prediction.
- Integrate biological and clinical features into predictive pipelines.
- Interpret model outputs for early detection and stratification use-cases.
- Address bias, imbalance, and ethical considerations in healthcare AI.
- Apply workflows to research projects, theses, or translational studies.
Fee Structure
Student Fee
₹1799 | $70
Ph.D. Scholar / Researcher Fee
₹2799 | $80
Academician / Faculty Fee
₹3799 | $95
Industry Professional Fee
₹4799 | $110
What You’ll Gain
- Live & recorded sessions
- e-Certificate upon completion
- Post-workshop query support
- Hands-on learning experience
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