Genomics & Precision Medicine
Structuring the language of the genome for AI-driven personalised care
Industry Challenge
Genomics AI — variant interpretation, polygenic risk scoring, pharmacogenomics, and cancer genomics — requires precisely structured, expert-annotated genomic data at a scale that manual curation cannot sustain. Clinically trained genomics specialists are needed to annotate variants for pathogenicity, interpret phenotype–genotype relationships, and structure biomarker datasets for precision medicine models.
How SCILabel Serves This Industry
Data Collection
We source whole-genome sequencing outputs, variant call files (VCF), clinical genomics reports, biomarker assay datasets, and phenotype-genotype databases from genomics research centres, biobanks, and clinical genetics laboratories through data partnership agreements.
Data Annotation & Labeling
Our Track 4 (Genomics & Biomedical) workforce — biotechnologists, biochemists, biomedical scientists, and laboratory scientists — annotates genomic variants with pathogenicity classifications (ACMG criteria: Pathogenic/Likely Pathogenic/VUS/Likely Benign/Benign), phenotype–genotype associations, biomarker relevance labels, and pharmacogenomics interaction annotations.
Data & Model Evaluation
Expert evaluators benchmark variant classification model concordance against ClinVar and LOVD databases, assess pharmacogenomics recommendation accuracy against CPIC guidelines, and test for population bias in polygenic risk score models.
Annotation Types & Formats
- Genomic variant annotation: pathogenicity classification (ACMG 5-tier)
- Phenotype–genotype association labeling
- Pharmacogenomics gene–drug interaction annotation (CPIC-aligned)
- Biomarker relevance classification for oncology panels
- Cancer somatic variant annotation: driver vs passenger, actionability tier
Specialist Workforce Tracks
Track 4 (Genomics & Biomedical): Biotechnologists, Biochemists, Biomedical Scientists, Medical Laboratory Scientists specialising in Molecular Biology.