Data Universe
Data Universe at your fingertips
Harness the power of unstructured and multimodal data to generate orthogonal insights that reinforce and diversify your scientific conclusions.
Data is the driver of innovation. Yet, 97% of biological and health data is fragmented and inaccessible, hampering research and discovery. Imagine what you could do if you had access to integrated, high-quality datasets that include multi-omics, clinical trials, and meta-data.
Our Data Universe services empower you to navigate complex multi-modal data with ease and accelerate life-changing breakthroughs, becoming your go-to multi-omics data platform.
Get the answers to your biological questions with integrated data solutions
Your challenge
Identify and collect all relevant data from literature
Harmonize metadata from multiple sources to common ontologies
Harmonize data from multiple sources to uniform quality and representation
Incorporate all internal and external data in your hypothesis validation
Prioritize candidate targets that result from initial analyses
Find a laboratory model for follow-up validation experiments
Derive or validate your findings across all available data modalities, utilizing effective multimodal data integration.
Solution
Ardigen
Data Universe
Biobank data
- 100,000+ subjects
- Clinical records
- Real-world evidence
- Multi-omic data
Public repository data
- 200,000+ research projects
- Disease-diverse
- Rich metadata
Ardigen internal data
- Transcriptome CRC cohort (86 subjects)
- Comprehensive Single cell atlases (40+ cell types)
- Gene and variant annotation data (20+ DBs)
- Microbiome data (600 + subjects)
Imaging, cell morphology
Large consortia data
- 100,000+ samples
- Multi-omic data
- Rich metadata
Value
Multi-omic data lakes for exploration
AI-derived novel targets
Prioritized candidate molecules/antigens
Optimized candidate molecules/antigens
Antigen prevalence, treatment modulation and outcome impact
Accessible biomarkers for disease progression, response to treatment, etc.
Matched comercial cell line/animal models for improved decision-making based on real-world evidence solutions.
FAQs
How does a multi-modal data platform improve the accuracy of AI predictions?
AI models trained on multi-modal data are better equipped to capture complex, non-linear relationships across biological layers. For example, integrating transcriptomic profiles with phenotypic imaging or clinical outcomes allows for more robust prediction of drug response, mechanism of action, or patient stratification. This reduces overfitting to a single modality and improves generalizability.
How can data from public repositories and consortia be used for drug discovery?
Public datasets (e.g., UK Biobank, TCGA, JUMP-CP, GTEx) offer rich sources of annotated, large-scale data that can be used to train, validate, or benchmark AI models. When properly harmonized and integrated with proprietary data, they enhance statistical power, fill knowledge gaps, and accelerate hypothesis generation for early-stage discovery and translational research.
What are the main challenges of data harmonization in life sciences?
Key challenges include inconsistent metadata, missing values, batch effects, incompatible formats, and differing ontologies. Ensuring semantic alignment across datasets from different technologies or sources requires domain expertise, robust pipelines, and often manual curation. Automation tools can help, but domain-specific QA/QC processes remain essential for scientific integrity.
What is the difference between structured and unstructured data in a clinical setting?
Structured data includes well-organized information such as lab values, ICD codes, or medication lists. Unstructured data refers to free-text notes, radiology images, pathology slides, or PDFs. Integrating both types requires advanced natural language processing (NLP), image analysis, and data annotation tools to unlock their full value for research.
How do you ensure data quality and standardization when integrating data from multiple sources?
Ardigen applies a multi-step quality assurance process that includes format validation, metadata normalization, batch effect correction, and ontology mapping. We use automated pipelines complemented by expert review to ensure consistency, reproducibility, and compliance with standards such as FAIR, CDISC, or OMOP, depending on project requirements.
How this benefits you: A researcher's perspective
Time-saving precision
Spend less time struggling with inconsistent data and more time making discoveries. With harmonized data and user-friendly access to integrated life science datasets, insights are within reach faster than ever before.
Stronger, actionable insights
Avoid the pitfalls of weak or fragmented data. High-end curation and expert multimodal data integration ensure that only the most relevant data points are surfaced, giving you confidence in your conclusions and driving deeper biological data insights.
Tailored for life sciences
The data isn’t generic — it’s tailored specifically for life sciences research. Focus on uncovering robust signals from disease-relevant data, without worrying about unnecessary noise or complexity.
Improved decision making based on real-world evidence
Enhance your research outcomes by integrating real-world evidence, clinical data, and patient cohorts. From discovery to clinical development, you decision making will be backed by tangible insights from integrated life science datasets.
Your project, our solution!
Contact us for the consultation.
Experimental model selection: Find the right model, faster
By integrating data from both client and public sources, we build virtual cohorts specific to your research. Using advanced AI techniques, we match profiles to the most relevant human and animal models, ensuring the best fit for your validation experiments, and providing AI for data insights.
Omics profile / signature
Aligned patient vs cell lines data
Model
List cell lines most representative for a given cohort. Full patient profile after alignment to cell lines
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