Our patented mathematical framework gives us an unparalleled ability to analyze huge multi-dimensional datasets and to accurately model the behaviour of complex systems
Our combinatorial analytics approach reveals many more of the biological drivers of disease than any other method.
It uniquely captures the non-linear effects of interactions between genes and other epidemiological and environmental factors as they interact across complex genetic and metabolic networks.
We generate much more signal from much smaller datasets than traditional Genome Wide Association Studies (GWAS) models, and we incorporate a wide range of multi-modal data including genomic, transcriptomic, clinical, environmental and epidemiological data. This allows us to better stratify patients according to the mechanistic origins of their disease.
Because it looks to find the independent effects of single SNPs, GWAS misses much of the real disease signal in polygenic diseases, making it difficult to stratify patients.
Single gene approaches such as GWAS have transformed the study of cancer and rare disease but haven’t had nearly the same impact on more complex chronic diseases. This is because GWAS is an inherently limited and low-resolution tool best suited to relatively simple monogenic diseases.
GWAS finds single SNPs that occur more often in a patient population than in healthy controls. It assumes the effects of those SNPs on a disease are independent, additive, and the same across all patients, ignoring subgroups and interactions between SNPs and genes. It focuses on rarer variants often not shared between different populations, especially those with different ethnic backgrounds.
Machine learning and polygenic risk models built on the results of GWAS cannot reconstruct the critical non-linear interactions and so are also incomplete and lack predictive power in chronic diseases, especially across populations with different origins.
Combinatorial analytics reveals many more valid genetic associations than GWAS and generates deeper disease insights from much smaller patient datasets than alternative AI drug discovery approaches.
As well as case:control studies we also apply our analytics to examine the features associated with a continuous phenotypic trait – for example retrospectively analyzing patients' responses to a drug candidate in a clinical trial to identify biomarkers that are predictive of drug response.
Our iterative approach starts from simple single feature associations, finding and validating new features to add to these to build ever more complex and predictive combinations. We use extensive statistical and biological validation methods at every stage to ensurethe significance, accuracy and biological relevance of our findings.
The combinatorial complexity of this approach is huge – we routinely deal with disease studies that require the testing of 1040-1050 potential combinations. However, our proprietary patented combinatorial analytics approach enables us to reduce this complexity in a principled and lossless manner that preserves biologically relevant signal.
From the combinations of factors we identify, we build a much more detailed picture of the complex, interconnected biology within chronic disease populations.
We capture the different mechanistic origins of disease biology and how these affect different subgroups of patients, to identify novel drug targets relevant for those individuals.
Our combinatorial analytics approach identifies the interactions between multiple variants and other clinical and epidemiological factors to enable precision medicine in chronic diseases.
These combinations exert a significant but unpredictable (non-linear) effect on a patient's phenotype. Because of this, a chronic disease population usually contains multiple patient subgroups with different disease etiologies, showing a wide spectrum of symptoms, severities, and therapy responses.
Our iterative approach starts from simple single feature associations, finding and validating new features to add to these to build ever more complex and predictive combinations. We use extensive statistical and biological validation methods at every stage to ensurethe significance, accuracy and biological relevance of our findings.
The combinatorial complexity of this approach is huge – we routinely deal with disease studies that require the testing of 1040-1050 potential combinations. However, our proprietary patented combinatorial analytics approach enables us to reduce this complexity in a principled and lossless manner that preserves biologically relevant signal.
From the combinations of factors we identify, we build a much more detailed picture of the complex, interconnected biology within chronic disease populations.
We capture the different mechanistic origins of disease biology and how these affect different subgroups of patients, to identify novel drug targets relevant for those individuals.
Our combinatorial analytics approach identifies the interactions between multiple variants and other clinical and epidemiological factors to enable precision medicine in chronic diseases.
These combinations exert a significant but unpredictable (non-linear) effect on a patient's phenotype. Because of this, a chronic disease population usually contains multiple patient subgroups with different disease etiologies, showing a wide spectrum of symptoms, severities, and therapy responses.
Combinatorial analytics offers the potential for transformational insight into the complexity of biology underpinning chronic diseases and complex health. The ability to identify and apply the non-linear signals underpinning complex biological processes could lead to much more widespread and accurate use of genomic and precision medicine approaches outside just the oncology and rare disease spaces.
The deepest repository of insights and IP into the biological mechanisms of chronic diseases
Our analysis of chronic diseases rapidly reveals insights that would usually take years of investigation
We work with collaborators worldwide and protect their most confidential and sensitive data
