Genetics

Genetic data is powerful, but it’s also messy—millions of variants, many of them correlated, and only a fraction with clear biological meaning. This blog post walks through a recent study that puts several machine learning approaches head-to-head (from logistic regression and random forests to deep neural networks) to see which ones reliably distinguish people with multiple sclerosis or Alzheimer’s disease using curated disease-linked variants. Beyond “who wins,” it focuses on what matters for real science: stability across datasets, performance under class imbalance, and whether the models point back to credible biology (especially immune-related signals and HLA involvement in MS). The takeaway is refreshingly practical: in this setting, simpler models can be not only competitive, but often more dependable—and easier to interpret—than more complex deep learning pipelines.