Editing AI for Genomics and Bioinformatics (section)

== <span style="color: #FFFFFF;">Analyzing</span> ==
{| class="wikitable"
|+ Genomics AI Application Maturity
! Application !! AI Approach !! Clinical Use || Key Metric
|-
| Protein structure prediction || AlphaFold (transformer + MSA) || Drug design || TM-score (>0.7 = reliable)
|-
| Variant pathogenicity || Ensemble ML, deep learning || Clinical genetics || AUC on ClinVar benchmarks
|-
| Cancer genomics || CNN on somatic mutations || Research/clinical || Driver gene identification accuracy
|-
| PRS disease risk || Penalized regression, Bayesian || Research → clinical || C-statistic (AUC)
|-
| scRNA-seq cell typing || Clustering + transfer learning || Research || F1 on held-out datasets
|-
| CRISPR off-target prediction || CNN, transformers || Research || AUROC on off-target sites
|}

'''Failure modes''': Batch effects — systematic technical differences between sequencing batches can dominate biological signal. Genetic ancestry bias — GWAS and PRS trained predominantly on European-ancestry populations perform poorly for other groups. Data leakage from protein databases (model trained on homologous sequences performs poorly on truly novel proteins). Interpretation of non-coding variants remains extremely challenging.
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