Data4Cure | Biomedical Intelligence

Semantic Data Integration

Semantic data integration, management and search to find data quickly

The Biomedical Intelligence® DataHub provides a comprehensive solution for managing public and internal data and analyses.

Thousands of pre-integrated public data resources
Easy integration and annotation of private data (intuitive UI and strong API support)
Fine-grained access control and sharing of data and analyses

Multidimensional system biology and machine learning-based analysis of clinical trial data

The Biomedical Intelligence® App Engine is routinely used in explorative analyses of complex multidimensional data from clinical trials.

Interactive analysis of multi-omics and clinical data
Advanced systems biology and machine learning apps
Contextual knowledge from thousands of prior datasets and millions of publications

Deciphering disease and drug response mechanisms through multiscale network and pathway analysis

Comprehensive analysis of transcriptomic and proteomic data to identify key pathways and networks in your condition of interest.

Differential expression and enrichment-based analysis of over 20 different pathway and network domains
Detailed pathway maps and hierarchical network views to facilitate interactive exploration and contextual understanding
Expanding beyond manually curated pathways to identify subnetworks and network modules underlying conditions of interest

Multidimensional data- and literature-based target discovery and validation

Target discovery and validation using continuously integrated knowledge extracted from thousands of datasets and millions of publications.

Target prioritization using multidimensional evidence summarized within and across data dimensions
Ontology-based hierarchical evidence integration and cross-referencing to facilitate verification and follow up analyses
Network and pathway views to facilitate contextual interpretation of integrated evidence

Global analysis of thousands of datasets to match drugs to disease signatures and help identify new indications

Leveraging the CURIE Knowledge Graph to find non-obvious connections between drugs and diseases and identify drugs which may reverse disease states.

Based on data-driven evidence for thousands of diseases and tens of thousands of compounds
Interpretable data-driven approach
Cross-referenced to underlying data evidence allowing users to understand why connections have been made

Multidimensional molecular disease subtyping and subtype assignment

Identification and interrogation of disease subtypes discovered from multidimensional molecular data. Subtype assignment for new samples.

Advanced algorithms for subtype identification and subtype assignment from multidimensional data
Interactive visualization tools to explore subtypes and underlying features
Direct integration of clinical data to identify associations between subtypes and clinical outcomes

Deep interrogation of cell type-specific mechanisms via single-cell and cell deconvolution analysis

Analysis and interactive exploration of single-cell RNA-seq data. Identification and quantification of cell types represented in bulk mRNA data.

Comprehensive pipelines for scRNA-seq data processing, clustering and cell type assignment
Ability to integrate CITE-seq, ATAC-seq, TCR/BCR sequencing and spatial transcriptomics data
Cell deconvolution analysis from bulk mRNA data using seven competing algorithms

Combinatorial biomarkers and predictive models via interpretable machine learning

Predictive models for identifying combinations of key predictors underlying disease, drug response or other clinical outcomes.