Revolutionizing AI for a healthier tomorrow
Ingine's Quantum Bayesian AI offers the ultimate solution for generating knowledge and predictive insights in the field of life sciences. It has the ability to transform both structured and unstructured data into probabilistic knowledge, enabling you to gain faster and more impactful insights. Our innovative approach revolutionizes decision-making, giving you a competitive edge, reducing costs, and increasing your market share. You can take your clinical trials and drug research to the next level.
Ingine is speed-to-knowledge
Ingine is an integrated system for knowledge generation and management, supporting automated reasoning, inference, and prediction that ensures the transparency and comprehension of all underlying analytics, allowing for faster insight and decision-making.
Our platform uses advanced applications and adaptations of Dirac notation and algebra (quantum mechanics), Bayes theorem, and other information theoretic concepts, 2nd order semantic ontologies, and the industry’s most powerful bi-directional graph data models.
Faster, More Cost-Effective AI Development
Ingine unlocks the full potential of life science data with lightning speed and minimal cost to improve the human condition.
- Eliminates data cleaning & algorithm training
- Supports Real World Data - uncertainty and sparse
- No trial-and-error factor weighting
- No assumptions of feature independence
- Full explainability
- 100% Probabilistic
- Support for Bayes Inference and coherence
- Knowledge builds with new findings
Life Sciences Use Cases
Knowledge generation from real-world data (RWD) to generate real-world evidence (RWE)
The Ingine platform supports many broad use cases throughout the drug development lifecycle.
- Go-to-Market Segmentation / Cohort Identification
(Helps identify patient subpopulations most likely to respond to products using explainability to understand efficacy and competitive advantages)
- Drug Re-purposing / Label Expansion
(Combines trial, patient data, SDOH data, and other proprietary enterprise data to find new opportunities quickly)
- Adverse Event Monitoring
(Incorporates patient and treatment data, empowering researchers to make better assessments of causative effects and feature interactions)
Why Ingine Is Different
Ingine is an innovative platform that integrates various mathematical and computational techniques, such as Hyperbolic Dirac Nets (HDNs) based on physicist Paul Dirac's quantum mechanics equations. The platform creates knowledge management and AI systems that are more transparent, flexible, and powerful in the field of medicine and healthcare. The platform is built on more than 40 years of intellectual property development and exclusive insights gained from our pioneering work in data science areas, including Bioinformatics, Protein Modeling, Computer-aided Drug Design, and AI.
Zero Black-Box Bias
The Ingine knowledge store and all predictions are fully explainable (XAI), linking probabilistic predictions to diagnostic reasoning to understand how actions (interventions) impact outcomes.
Represents probabilistic knowledge as Quantum Universal Exchange Language (Q-UEL) tags that capture high dimensionality and relationships (conditional, bi-directional, and cyclic interactions) beyond just "IF" and dynamic reasoning.
The building block for our predictive models is a repository of reusable knowledge from many sources, including electronic medical records, claims, medical literature, omics (bio-molecular), and genetic and chemical datasets.
Ingin'e implements a second-order semantic interoperability framework (SIF) to maintain contextual probabilistic knowledge from any data source and preserve knowledge representation and semantic consistency - eliminating the need for time-consuming and expensive data cleaning and trial-and-error processes for creating weighted assumptions.
Universal Exchange Language
Ingine's universal exchange language (Q-UEL) captures probabilistic knowledge from complex systems, such as human biology and health. Q-UEL tags (similar to XML) are semantic statements that can be manipulated symbolically and arithmetically.
Real World Data
Data mining methodology handles sparse and irregular real-world data by using Riemann zeta functions to integrate probabilities over multiple Bayesian perspectives informed by data counts.
Retrospective Cohort Studies
Our "Glass Box" machine learning for retrospective cohort studies, in conjunction with ETRI, establishes a Diagnostic Odds Ratio (DOR) and a new measure, DOR*, to evaluate outcomes in retrospective cohort studies analyzing medical records data.
Ingine is a software company based in Cleveland, Ohio, USA.
Co-founded by the former Chief Science Officer of IBM's Global Life Sciences, Healthcare, and Pharma division, Ingine is a novel platform using adapted Dirac algebra and notation, information theory, and Bayesian inference for faster, less expensive, and more accurate predictive analytics for life sciences and human health.
Ingine's mission is to unlock the full potential of life science data with lightning speed and minimal cost to improve the human condition.