Multi-Omics Feature Store

Ensures features are computed using only data available at a specific historical timestamp, preventing data leakage from the future. This is critical for training survival analysis models where knowing a patient's later outcome would invalidate the predictor.

• Implements time-travel queries for reproducible training datasets
• Prevents the subtle bug of using post-diagnosis data to predict a diagnosis
• Essential for regulatory compliance in clinical trial patient stratification

Transforms raw omics signals into ML-consumable feature vectors through standardized pipelines. The store manages the computational heavy lifting so data scientists query transcriptomics.normalized_counts rather than wrangling FASTQ files.

• Bulk operations: Compute pathway enrichment scores across 10,000 samples
• Cross-modal joins: Combine somatic mutation burden with radiomics texture features
• Embedding serving: Store and retrieve pre-computed VAE latent vectors for rapid model inference

Every feature is an immutable, versioned asset with full provenance tracking. If a normalization parameter changes, the feature store does not overwrite history—it creates a new version, linking it to the specific harmonization protocol and Git commit that produced it.

• Trace any prediction back to the exact feature set and preprocessing code
• Roll back to a previous feature version if a new one introduces drift
• Audit trails satisfy FDA software as a medical device guidance

Bridges the gap between batch training and real-time inference. The store materializes features in a low-latency online store (e.g., Redis, DynamoDB) for clinical decision support APIs while maintaining a historical offline store (e.g., Parquet on S3) for model training.

• Training: Query millions of rows with complex joins in Spark
• Inference: Retrieve a single patient's fused multi-omics embedding in < 10ms
• Eliminates the training-serving skew caused by duplicate feature logic

Acts as the single source of truth for feature logic, registered once and reused across all models. A tumor mutational burden score is defined in the store, not buried in a notebook, ensuring the oncology risk model and the immunotherapy response predictor use identical calculations.

• Feature registry: Central catalog with metadata, owners, and SLAs
• Reuse: Share features across biomarker discovery and clinical trial matching models
• Prevents the divergence that occurs when teams independently re-implement complex bioinformatics logic

Applies pre-computed normalization and batch correction transforms at serving time. The store ensures that a feature for a new patient sample is projected into the same harmonized space as the training data, using stored parameters from ComBat or Harmony algorithms.

• Store correction matrices as versioned artifacts alongside features
• Apply the exact same quantile normalization used during training
• Critical for multi-center federated learning deployments where site effects must be removed transparently

The process of transforming raw molecular counts into model-ready tensors. In multi-omics, this involves handling the p >> n problem (more features than samples).

• Log-fold change normalization to stabilize variance
• Batch effect correction using ComBat or Harmony
• Dimensionality reduction via PCA or autoencoders before storage
• Missing value imputation using KNN or variational methods

The feature store must version these transformations to ensure reproducibility.

The computational identification of a minimal, robust subset of molecular features predictive of a clinical endpoint. The feature store must support:

• Filter methods: Variance thresholds, mutual information
• Wrapper methods: Recursive feature elimination with cross-validation
• Embedded methods: LASSO and elastic net regularization
• Stability selection: Features consistently selected across bootstrap samples

Storing feature importance scores alongside the features enables audit trails for regulatory submissions.

Systematic non-biological variation introduced by differences in sample processing, sequencing platforms, or reagent lots. A production feature store must:

• Store batch metadata as first-class entities
• Apply Harmony or limma corrections at ingestion
• Version the correction model applied to each feature
• Flag features with high batch-associated variance

Without this, models learn technical artifacts instead of biology.

A standardized computational pipeline ensuring technical compatibility across heterogeneous multi-omics datasets. Key components:

• Common data model: Mapping diverse schemas to a unified ontology
• Identifier mapping: Translating gene symbols, Ensembl IDs, and probe IDs
• Unit normalization: FPKM, TPM, or raw counts with offset
• Quality control gates: Automated checks for sample swaps or contamination

The feature store enforces these protocols at write time.

Community-driven specifications for describing experimental context. Examples include MIAME (microarrays), MIAPE (proteomics), and FAIR principles (Findable, Accessible, Interoperable, Reusable).

A feature store must:

• Enforce metadata completeness before ingestion
• Index metadata for querying ("all samples with KRAS mutation")
• Link features to their provenance metadata
• Support ontology terms from SNOMED CT or LOINC

Statistical removal of extraneous variable influence that creates spurious molecular associations. Common confounders in multi-omics include age, sex, smoking status, and cell-type composition.

The feature store should:

• Store raw and adjusted feature values separately
• Track the covariates used in each adjustment model
• Support propensity score and inverse probability weighting
• Enable sensitivity analysis across different adjustment strategies