Editing Ai Genomics (section)

== <span style="color: #FFFFFF;">Understanding</span> ==
Genomics is inherently a machine learning problem: biological sequences are discrete strings over small alphabets (A, T, G, C for DNA; 20 amino acids for proteins), and the relationship between sequence and function is complex, non-linear, and learned from evolutionary history across billions of years.

'''The protein folding revolution''': For 50 years, predicting a protein's 3D structure from its amino acid sequence was considered one of biology's grand challenges. DeepMind's AlphaFold2 (2020) solved this with near-experimental accuracy, using a combination of multiple sequence alignment, equivariant attention networks, and self-supervised learning on known protein structures. AlphaFold has predicted structures for virtually all known proteins — ~200M structures — in the AlphaFold Protein Structure Database, transforming drug discovery.

'''Genomic language models''': BERT-style transformers pre-trained on DNA sequences learn rich representations of genomic function. Models like Enformer predict gene expression from DNA sequence by learning the regulatory grammar encoded in non-coding regions. DNABERT, Nucleotide Transformer, and Evo (trained on billions of DNA sequences) have achieved state-of-the-art on diverse genomic prediction tasks.

'''Single-cell AI''': scRNA-seq measures gene expression in individual cells, generating sparse high-dimensional count matrices (cells × genes). AI tools like Seurat and Scanpy cluster cells by type; foundation models for single-cell data (scGPT, Geneformer) enable zero-shot cell type annotation, perturbation prediction, and drug response modeling.

'''Polygenic Risk Scores (PRS)''': Aggregating thousands of small-effect genetic variants into a single disease risk score. Modern PRS methods use penalized regression (LASSO) and Bayesian approaches on GWAS summary statistics. PRS can predict cardiovascular disease, type 2 diabetes, and schizophrenia risk years before clinical onset.
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