Model Card

Basic metadata including the model's name, version, release date, and owning organization. This section establishes a unique identifier linked to the Model Registry entry, ensuring that every deployed artifact is traceable to its exact training run and source code commit. It typically includes the architecture type (e.g., Transformer, Graph Neural Network) and the specific genomic task it performs, such as variant calling or gene expression prediction.

A precise definition of the biological context where the model is validated to perform. This includes the sequencing technology (e.g., Illumina short-read, PacBio long-read), the species and genome assembly version, and the specific clinical or research task. Critically, it explicitly lists out-of-scope uses, such as applying a model trained on European populations to underrepresented ancestries, to prevent unsafe extrapolation.

Quantitative metrics broken down by population cohort and genomic region. This section reports standard statistics like precision, recall, F1-score, and AUROC on held-out test sets. It must detail the demographic composition of the evaluation data to expose potential performance disparities. For variant callers, this includes performance on difficult-to-map regions like MHC and segmentally duplicated genes.

A high-level description of the datasets used for pre-training and fine-tuning, including their sources (e.g., gnomAD, 1000 Genomes, UK Biobank), sample sizes, and known biases. This section links to the ML Metadata Store and Feature Store to provide full data lineage. It must disclose whether synthetic data generated by a Generative Adversarial Network was used to augment training.

A candid assessment of risks, biases, and failure modes. This includes the potential for ancestry-specific false negatives in clinical diagnostics and the model's inability to generalize to novel structural variants not seen in training. It addresses privacy risks, confirming whether Differential Privacy guarantees were applied during training and the potential for membership inference attacks against the training data.

Results from feature attribution methods like DeepSHAP or in-silico mutagenesis that explain which input sequence motifs drive the model's predictions. This section demonstrates that the model is learning biologically relevant signals—such as canonical transcription factor binding sites—rather than confounding batch effects. It supports Algorithmic Explainability requirements for clinical auditability.

Model cards provide auditable evidence for regulatory submissions to bodies like the FDA and EMA when genomic models are deployed as Software as a Medical Device (SaMD). They document the model's intended use, target population, and performance characteristics required for 510(k) clearance or CE marking.

• Captures performance stratified by ancestry group and sequencing platform
• Documents training data provenance including cohort demographics and exclusion criteria
• Records known failure modes such as reduced accuracy in underrepresented populations
• Serves as a living document updated with post-market surveillance data

When genomic models inform variant pathogenicity classification or treatment selection, model cards communicate performance characteristics to clinical geneticists and molecular pathologists. They detail the model's positive predictive value and false negative rate for clinically actionable variants.

• Specifies confidence calibration metrics for variant calls
• Lists genes and variant types within the model's scope of competence
• Documents validation against orthogonal technologies like Sanger sequencing
• Clarifies that the model is assistive, not autonomous in clinical workflows

Model cards quantify performance disparities across genetic ancestry groups to prevent biased variant calling. They report metrics like sensitivity and specificity stratified by 1000 Genomes superpopulations and gnomAD ancestry labels, exposing where models underperform due to training data imbalances.

• Reports per-ancestry F1 scores for variant detection
• Documents representation statistics of training cohort demographics
• Identifies performance cliffs in populations with limited reference data
• Enables fairness-aware model selection for diverse patient populations

Model cards serve as a versioned artifact in genomic MLOps pipelines, capturing the exact training configuration, data splits, and evaluation methodology required to reproduce results. They integrate with model registries and ML metadata stores to maintain an immutable audit trail.

• Records training data version and preprocessing parameters
• Documents hyperparameters, random seeds, and hardware configuration
• Links to container images and environment specifications
• Enables rollback and comparison between model versions in production

Model cards translate technical performance metrics into actionable information for diverse audiences: bioinformatics engineers need AUROC and precision-recall curves, clinicians need positive predictive value, and patients need plain-language explanations of model reliability.

• Provides technical deep-dive sections for ML engineers and bioinformaticians
• Includes clinician-facing summaries with actionable performance context
• Offers patient-accessible language explaining model limitations
• Documents ethical considerations and institutional review board approvals

Model cards enable downstream consumers—such as clinical pipeline developers and pharmacogenomic applications—to evaluate whether a genomic model is fit for their specific use case. They document out-of-distribution behavior and domain shift sensitivity critical for safe integration.

• Specifies input data requirements including sequencing depth and read length
• Documents expected degradation under noisy or low-coverage inputs
• Lists incompatible use cases and contraindications
• Provides integration guidance including expected latency and throughput

The continuous monitoring process that identifies when a deployed genomic model's predictive performance degrades due to changes in the underlying data distribution. Drift detection triggers the review cycle for updating a model card's stated limitations.

• Compares production input distributions against the training data baseline
• Alerts when prediction confidence shifts beyond acceptable thresholds
• Informs the 'Known Limitations' section of the model card with empirical evidence
• Essential for maintaining the accuracy of a model card's performance claims over time

A system that statistically compares the distribution of incoming production genomic data against the training data baseline. It provides the quantitative evidence needed to update a model card's fairness and performance disclosures.

• Uses statistical tests like Kolmogorov-Smirnov or Wasserstein distance
• Detects shifts in population demographics, sequencing platforms, or lab protocols
• Triggers alerts for potential model performance decay in specific subpopulations
• Directly feeds the ongoing validation of a model card's intended use claims

Feature attribution and saliency map techniques for decoding the decision logic of genomic neural networks. Interpretability methods generate the explanatory content required for a model card's technical transparency section.

• Integrated Gradients and DeepLIFT attribute predictions to input sequence motifs
• In-silico mutagenesis maps reveal which nucleotides drive variant pathogenicity calls
• Attention weight visualization exposes the sequence context the model relies upon
• Provides the mechanistic evidence that substantiates a model card's performance benchmarks