Computational Biology
Machine Learning for Genomics
Predictive models for regulatory effect, protein structure and clinical phenotype.
Scientific context
Understanding the field
Model development is paired with rigorous benchmarking, calibration analysis and explicit assessment of the limits of prediction in clinical contexts.
Machine learning for genomics builds models from sequence, molecular and clinical data to predict patterns that are difficult to encode manually. Responsible development emphasises external validation, calibration and interpretable limitations.
Central questions
- Do predictions generalise beyond the training dataset?
- Which biological evidence supports a model output?
Methodological framework
- Sequence and multimodal representation learning
- Held-out benchmarking and external validation
- Calibration, attribution and bias analysis
Relevance
Scientific and clinical value
Well-evaluated models can prioritise experiments, organise complex data and support review, but their role should remain proportionate to demonstrated performance and the consequences of error.
Limits and responsibility
Models can learn technical artefacts, ancestry imbalance and historical bias. High benchmark accuracy does not establish clinical validity, causality or safety in a new population.
Authoritative resources
Public reference resources
These independent resources are provided for scholarly orientation; inclusion does not imply an institutional partnership. This page does not replace medical advice or diagnosis.