Integrated Correlation Engine (iCE)

iCE uses a novel data correlation models that facilitate the investigation of relationships between multi-omics and clinical datasets – accelerating the identification of clinically relevant biomarkers

What is ICE

iCorrelation engine (iCE) executes AI-powered correlation and analysis of multidimensional omics and clinical data. Facilitates the biomarker discovery and patient stratification process.

Who is it for

Study managers, clinical operation managers, clinical research coordinators, scientists, clinical investigators, clinicians, oncologists, cancer researchers, principal investigators, lab personnel, bioinformaticians


  • The integrated analysis of multi-omics data is a challenging task due to the heterogeneity in the different informatics platforms used.
  • Superimposition of multiomic datasets with clinical datasets is very complex and requires highly nuanced tools that can delve deeper to conduct rational and revealing correlative studies.
  • Increased exploratory analysis and biomarkers screen incentivizes programs generate to acquire multi-omics data at the outset exploratory recent FDA guidelines encouraging exploratory analysis and.
  • Many small-to-mid-sized pharma companies moving into the world of biomarker and exploratory trials, they lack the powerful bioinformatics tools to conduct such exploratory studies. This has led to many high profile failures of major I/O therapies trials conducted by pharma companies previously.


  • By executing an AI-powered integration, organization, visualization, and analytics of multi-dimensional data; the Integrated Correlation Engine (iCE) automate the biomarker discovery process.
  • iCE uses a novel data correlation models that facilitate the investigation of relationships between multi-omics and clinical outcomes datasets. This results in an improved ability to associate biomarkers with clinical phenotypes of interest.
  • iCE’s statistical integrative framework can benefit a diverse range of users like researchers, oncologists, principle Investigators, and bioinformaticians involved in immuo-oncology translational studies by enabling them to conduct Module-based analysis (MBA)
  • Graphical outputs greatly assist in the interpretation of complex analysis done on multiomics and clinical data. These features provide valuable biological insights and mine for patient-specific biomarkers in an unbiased manner as a function of treatment duration and dose.
  • We have designed adaptors and application programming interfaces (APIs) for a wide variety of “omics” datasets such as whole exome sequencing (WES), RNA-Seq, Nanostring, T-cell receptor (TCR) Seq, qPCR, Flow Cytometry, Immunohistochemistry (IHC), Cytokine profiles, Radiology (including images), Multiplex ELISAs, LC/MS, and clinical endpoints (PET, CT, MRI) and information derived from electronic data capture (EDC)/Argus.

Key Features

  • Insights from omics datasets can help mine previously unexplored biomarker at specific time points and predict response to the therapy.
  • Ability to run Principal component analysis (PCA), identify specific clusters of biomarkers, and identify relevant pharmacogenomic cohorts within the patient subpopulation
  • Ability to conduct retrospective screening on specific time points and identify previously missed predictive biomarkers.

iCE Dashboard displaying a relative expression of ten different genes in three cohorts of patients (progressive disease, partial response, and stable disease). The current output shows information at a specific time point derived from an RNA-Seq experiment. Notice drop-down menus and interactive scale for adjusting a number of markers and types of analysis (modality). This enables mining of similar or new gene expression changes from other types of experimental queries. Analysis like these form a basis for quick identification of biomarker trends for a particular patient cohort (e.g. responders vs. non-responders)

iCE allows the user to normalize the data as per their requirements. Also recommends normalization parameters that will give the best results.

iCE is powered to conduct multi-omics correlation analysis enabling researchers and scientist to identify biomarkers and stratify patients according to their response and phenotype

iCE can conduct complex analysis and displaying data in several graphical outputs. Shown here are two representative graphical output format. On the left notice, a scatter plot showing a correlation between p-value and the change in gene expression measured in fold change in for a particular experiment. Shown on the right is a dot-plot distribution of samples (cohort of patients with observed metastasis)

iCE is powered to conduct principal component analysis (PCA) on multi-omics and clinical data – enabling researchers to identify powerful clusters of genes (notice marker cluster and PID cluster) associated with a particular phenotype.