Academy

Machine Learning in Biomedical Research

Machine learning (ML) refers to computational techniques that enable computers to learn patterns from data and make predictions or decisions without explicit programming. In biomedical research, ML algorithms process complex datasets—such as genomic, transcriptomic, and imaging data—to identify disease markers, predict patient outcomes, and guide therapeutic development.

Key Concepts:

• Supervised learning: Algorithms learn from labeled datasets (e.g., patient versus healthy) to classify new samples. Common methods include decision trees, support vector machines, and gradient-boosting models like XGBoost.
• Unsupervised learning: Techniques such as clustering group data without predefined labels, revealing natural patterns in gene expression or cell populations.
• Feature importance: Measures each input variable’s impact on model predictions, helping researchers pinpoint the most informative biomarkers.

How Machine Learning Works with RNA-Seq Data

RNA sequencing (RNA-Seq) generates large volumes of count data, where each gene’s expression level is represented by the number of sequencing reads. ML models process this high-dimensional data to detect differentially expressed genes and classify samples based on disease status.

1. Data preprocessing: Quality control removes low-quality reads and normalizes count values to correct for sequencing depth.
2. Feature selection: Statistical tests (e.g., p-values, fold changes) and ML-based methods (e.g., XGBoost feature importance) rank genes by relevance.
3. Model training: Algorithms learn classification rules on training data, optimizing parameters to minimize error.
4. Validation: Performance is assessed on independent test sets using metrics like accuracy, precision, recall, and ROC AUC.

Applications in Disease Diagnostics and Therapeutics

ML has enabled breakthroughs across various biomedical domains:

• Infectious diseases: Rapid identification of pathogens and host response markers, as demonstrated in tuberculosis RNA-Seq classification pipelines.
• Cancer: Prediction of tumor subtypes, patient prognosis, and drug response from genomic and imaging data.
• Drug discovery: Virtual screening of compounds against molecular targets and repurposing existing drugs through interaction networks.

Challenges and Future Directions

Despite its power, ML in biomedicine faces hurdles:

• Data quality and bias: Incomplete or imbalanced datasets can skew model predictions.
• Interpretability: Complex models like deep neural networks may act as “black boxes,” making it hard to trace decision pathways.
• Integration: Combining multi-omics, clinical, and imaging data requires robust frameworks and standardized formats.

Future efforts will focus on improving model transparency, harmonizing diverse data sources, and translating ML discoveries into clinical applications for precision medicine.