Case studies: ValueData in real R&D environments
Explore how ValueData supports life-science teams with rigorous analytics, reusable expertise, and decision-ready outputs.
Identifying cardiac risk in COVID-19 through multimodal clinical and immunological data analysis
By combining routine clinical variables with immune profiling and machine learning, the study reveals practical predictors of myocardial damage, disease severity, and survival in COVID-19 patients.
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Reducing costly biomaterial experiments with AI-driven recommendations
EVA-KEE combines Bayesian Optimization and Evolutionary Algorithms to propose the next best experiments, enabling faster iteration in complex bio-production engineering.
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Standardizing AI-driven image analysis for 2D assays & 3D organ models
Our unified AI workflow processes complex morphological images across different dimensions to generate robust, standard readouts for drug screening.
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Accelerating Immunology High-Content Screening with AI
We developed an automated pipeline to extract reliable, multiplexed morphological signatures from high-throughput cellular screening.
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Enhancing drug-hit identification using multimodal AI
Combining chemical structure data with phenotypic imaging to identify promising therapeutic candidates with higher confidence.
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Improving patient stratification through Foundation Model embeddings
We leverage deep learning embeddings to unearth hidden subgroups and robustly stratify patients from high-dimensional multi-omics profiles.
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Revealing early COPD immune dysfunction through multimodal single-cell analysis
By combining patient-derived lung and blood samples with single-cell transcriptomics, machine learning, and functional assays, the study uncovered dysfunctional alveolar macrophage states and systemic immune alterations in early-stage COPD.
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