How is machine learning applied in bioinformatics?

Machine learning is widely applied in bioinformatics for prediction and classification: 1) Sequence-based predictions - protein secondary structure (PSIPRED), subcellular localization, gene prediction, splice site detection. 2) Structure - protein structure prediction (AlphaFold), protein-ligand binding. 3) Function prediction - enzyme function, drug-target interactions. 4) Expression analysis - cell type classification, biomarker discovery. 5) Variant interpretation - pathogenicity prediction (CADD, REVEL). Methods include: Random Forests (feature interpretability), SVMs (small datasets), Neural Networks/Deep Learning (large datasets, complex patterns), CNNs (sequence motifs), RNNs/Transformers (sequential data). Key considerations: feature engineering, cross-validation, overfitting prevention, biological interpretability.