The iPhronesisTM platform allows clinicians, geneticists and bioinformaticians to work together seamlessly on data analysis, to develop methods and workflows using sequenced patient data.    Read more »

Use Cases

  • Cancer Genomics / Tumor Profiling: Tumor signatures that are multi-omic with evidences for biologically significant events
  • Rapid patient stratification for translational research: Apply powerful cohort signatures to accurately define patient sub populations for risk assessment
  • Diagnostic Odyssey: Identification and classification of rare variants, clinical diagnosis and clinical reports
  • Genotype / Phenotype Studies: Cause-to-effect analysis from deep systematic literature review and structured databases
  • Probability Scores
  • Mutation discovery: Discover new mutations and establish its pathogenicity using ACMG guided classification.
  • Relationship extraction: Text mining, interpretations, provides literature evidences
  • Predictive and prognostic reporting
  • Interactive pathway mapping: Understand the effect of mutational changes on cellular dynamic pathways
  • Biomarker and therapeutics discovery: Combine multi-omic and unstructured data to new relationships, e.g. efficacy & toxicity
  • Diagnosis and point of care analytics: integrate EMR/HMR, publications and personal genomics
  • NGS Variants: Utilize ontologies, semantics, taxonomies




Accelerate microbiome product development using Ai to improve decision support and turn-around time of data analysis. Researchers can stay on top of new publications by using the cognitive crawler to extract relevant scientific content.    

Use Cases

  • Microbiota characterization using a multitude of data sources
  • Quickly access contextually relevant insights by combining structured and unstructured information
  • Generate novel insights such as mode and mechanism of action
  • Retrieve detailed information about microbial pathways and antibiotic interactions
  • Build custom ontologies for specific product development

Pharma / Real World Evidence

A Real World Evidence approach is mandatory for pharma and biotech to achieve their organizational goals. iPhronesisTM gathers and automates disparate real world data to derive insights for pre-clinical research and clinical treatment applications.    Read more »

Use Cases

  • Drug Repurposing: Identify new drug purposes through the integration of clinical, genomic and phenotypic information
  • Disease Signature / Gene Signature: Develop accurate signatures of diseases combining gene expression pattern across a spectrum of experimental conditions
  • Drug Discovery: Pathway and structure analysis
  • Cohort Design: Stratify patients and build cohorts in seconds and minutes
  • Protocol Design: Design new protocols and validate feasibility in seconds
  • Clinical Trials Management: Operations planning through shared sponsor/provider dashboards
  • Smart Clinical Trial Design: Speed TTM and lower costs by accelerating the process shedding months/years off pipeline
  • Efficacy & Toxicity Prediction: Connect to EMR’s, advanced data models and advanced visualizations
  • Predictive and prognostic reporting
  • Interactive pathway mapping
  • Biomarker and therapeutics discovery: Combine multi-omic and unstructured data to new relationships, e.g. efficacy & toxicity





iPhronesisTM enables the development of treatment strategies centered on the ability to predict which patients are more likely to respond to specific therapies. iPhronesis creates a holistic view of the patient, combining patient demographics and associated clinical data, along with genetic factors for associated drug metabolism, drug response and drug toxicity to establish a clinical decision support process.    Read more »

Use Cases

  • Increase ROI for Hospitals: Using terms mapper, rapidly annotate clinical notes with IDC-10 codes using NLP
  • Longitudinal patient views: Detailed information about all significant medical events spanned over time. Detailed drilldown with contextually sensitive alerts and literature evidences.
  • Clinical decision support system: Powerful, validated data points from agnostic datasources for guiding decisions based on contextually sensitive inputs
  • Precision Medicine: Combining multi-omic data, generating rich annotated knowledgebases for executing patient profiles leading to patient specific outcomes and reporting
  • Personalized cancer treatment: Combining multi-omic data for determining toxicity/ efficacy of treatment and patient profile-based treatment
  • modifications. Evidences provided from primary literature and other databases.
  • Regulatory compliance policy design
  • Health data predictions