Multiomics data integration for drug discovery
Connect omics, imaging, screening and clinical evidence through shared identities, modality-aware processing and reusable data models.
Ardigen delivers multiomics data integration for discovery and translational teams that need to analyze evidence across molecular layers and other modalities. We align samples, patients, compounds or models; apply fit-for-purpose preprocessing; preserve provenance; and build integrated data products that support cross-modal questions.
The bottlenecks in multimodal programs
Modalities use different identities
Imaging, omics, screening and clinical records may describe the same sample, patient, compound or model without a reliable shared key.
Preprocessing choices are not comparable
Batch correction, feature extraction, normalization and QC differ across platforms, studies and teams.
Metadata is incomplete or inconsistent
Instrument, assay, sample and cohort context is not represented consistently enough for cross-modal interpretation.
Integration is rebuilt for every analysis
Scientists aggregate files and recreate join logic each time a new question is asked.
Single-modality conclusions lack context
A molecular or phenotypic signal may remain difficult to interpret without evidence from another modality or outcome layer.
Integration designed around the question and the entities
Multimodal integration is not simply storing datasets in one environment. It requires an explicit model of the entities that connect them, the transformations that make comparisons valid and the missingness or bias that limits interpretation.
Ardigen begins with the scientific question and the modalities available. The team defines cross-modal identities, selects data models and standards appropriate to the scope, applies modality-specific QC and normalization, and preserves source and transformation provenance.
The result is evidence that scientists can query across modalities and that data and AI teams can reuse without reconstructing the integration layer. Where appropriate, existing models or standards such as OMOP CDM or GA4GH Cat-VRS can inform the design, subject to the actual data and use case.
A connected framework for multimodal integration
Question, modality and data-model assessment
Inventory available data, linking keys, metadata, storage, preprocessing history and integration gaps. Select the entity and data-model approach before building joins.
Modality-specific preparation
Apply justified QC, normalization, batch handling, feature extraction or variable mapping for each data type without erasing meaningful biological variation.
Cross-modal alignment
Link samples, patients, compounds, perturbations or experimental models through stable identifiers, mapped metadata and explicit handling of missing modalities.
Governed integrated data product
Deliver a versioned schema, provenance, quality rules, access conditions and update procedures so the integrated asset can be reused.
Map Your Current Integration Gaps.
Concrete, reusable deliverables
Integration assessment. Inventory of modalities, entities, metadata, preprocessing histories, linking gaps and scientific constraints.
Cross-modal data model. Shared entity definitions, identifiers, relationships and selected standards with decisions documented.
Modality-specific processing components. Versioned QC, normalization, feature or variable-mapping steps appropriate to the agreed scope.
Integrated data product. Schema-defined, versioned and access-controlled output with provenance, quality rules and explicit missing-modality handling.
Update and reuse playbook. Procedures for onboarding new data, testing transformations, extending modalities and preserving prior analyses.
Cross-modal questions become repeatable
Teams work from documented relationships and transformations instead of rebuilding aggregation for each analysis.
Evidence can be interpreted in context
Molecular, phenotypic, screening and clinical signals can be compared with their provenance and study constraints visible.
New data extends an existing model
Additional studies or modalities follow defined identities, schemas and quality rules rather than creating another isolated asset.
Downstream teams receive stable inputs
Analysis, visualization and model development can reference versioned integrated data with explicit assumptions.
Frequently Asked Questions
Are datasets integrated if they are stored in the same cloud environment?
No. Colocation does not establish shared identities, comparable preprocessing, aligned metadata or reusable relationships. Integration defines those elements and documents how each modality can be interpreted with the others.
Which modalities can Ardigen integrate?
Potential scope includes genomics, transcriptomics, proteomics, metabolomics, epigenomics, single-cell, spatial data, mass cytometry, genotyping, imaging, morphological profiling, screening and appropriately governed clinical variables. The final list depends on verified delivery experience, available metadata and study design.
Which public datasets can be included?
Relevant open or controlled-access sources may be incorporated when the client has or obtains the required rights. Examples should be selected for the scientific question and approved access, not presented as universally available through Ardigen.
How do you choose a data model or standard?
The choice follows the entities, modality, downstream consumers and existing client environment. Ardigen can use internal patterns and established standards where they fit, but the design should not force a clinical, variant or other model onto data it does not represent well.
Does this service include multimodal analysis or model development?
The integration deliverable creates a reliable cross-modal evidence layer. Statistical analysis, target or biomarker research and model development can follow as separate scientific workstreams with their own objectives and validation.
Related capabilities
Data Harmonization & Curation
Standardizing datasets before and within multimodal integration
Data Sourcing & Partnerships
Identifying and accessing the external datasets that extend multimodal coverage
Contact
Ready to transform drug discovery and development?
The quality of cross-modal evidence depends entirely on how well the integration is engineered. Let us assess where your current setup creates bottlenecks, and what a governed multimodal data foundation would enable.
Send us a message and we will contact you back within 48 hours.
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