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From target discovery to clinical trial analysis, the Biomedical Intelligence Cloud is here for your research needs.
12+ apps to analyze public and private data
Integrated knowledge from thousands of datasets and millions of publications
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 (UI and API access)
Fine-grained access control and sharing of data and analyses
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 prior knowledge from thousands of prior studies and millions of publications
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 curate pathways to identify subnetworks and network modules underlying conditions of interest
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
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
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
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 and BCR sequencing data
Cell deconvolution analysis from bulk mRNA data using seven competing algorithms
Predictive models for identifying combinations of key predictors underlying disease, drug response or other clinical outcomes.
Advanced algorithms for building predictive models from multi-omics data
Interactive exploration of trained models and underlying evidence
Emphasis on model interpretability
We are continuously working on new apps and functionalities and we love to get requests from our users. Our scientific and software development teams are always ready for a new challenge. Let us know how we can help!
Sound science
Agile development cycles
Strong technical team
Responsive and open-minded
Leverage the Biomedical Intelligence® Cloud to turn data into actionable knowledge driving new cures.
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