Genteract: Data in, Diagnostics out

Clinical Hold

Genteract CVGxE analyzes clinical trial data to create highly accurate molecular diagnostics that genetically identify patient subgroups who experience fewer side effects... 551-209-2293

Resurrecting Drugs Stalled in Phase II or III

Genteract automatically creates diagnostics which predict which patient subgroups will experience a clinically significant response, with acceptable side effects ... More

Drug De-Risking and Targeting

Genteract CVGxE creates diagnostics that optimize population targeting, allowing patients to be matched with the drugs that will work best for them... aggrade

Drug Repositioning

Genteract analysis works with compounds at any stage of their lifecycle: during development, in market, or generic phase. CVGxE can find unexpected and valuable new indications... More

Genteract Analysis

Genteract analyzes datasets containing genotype or sequence data, with phenotype and environment data, to create diagnostics that predict how individuals will respond to an environmental stimulus, based on their genetics: a Gene-Environment Interaction (GxE).
For Pharma applications, the environment is the drug, the phenotype is the patient’s response to the drug.

Whole-Genome Analysis

Predictions are based on all GxE (Gene-Environment) signals for each interaction (often hundreds to thousands of different genomic positions). Previous methods make predictions based on one or two individual loci, which typically do not predict a large fraction of response on their own and are therefore not generally applicable.

Increased Statistical Power

Highly increased statistical power over previous methods, enabling more accurate predictions using smaller datasets.

Continuous Variable Analysis

Our methodology interprets continuous phenotype and environmental variables, rather than relying on case-control logic that is not appropriate for most complex traits.

Any Dataset

Genteract analysis works with any dataset containing genomic data, phenotypic data, and environmental data (which describes most clinical trial data and many other datasets), to discover new associations which were undetectable previously.

Any Format

Genteract Analysis supports genotype data, sequencing data, time-series data, and can work with standard or non-standard formats.

Smaller datasets

Genteract Analysis can extract meaningful results from datasets which are smaller (hundreds rather than thousands of individuals) than those used by traditional analysis. This means that many existing clinical trial datasets can be easily analyzed to create diagnostics.

Genteract Analysis finds what other analyses miss.
Here's an example: conventional analysis finds no association between Vitamin A and Body Mass Index.
Genteract analysis of NIH data discovers two genetically-defined subgroups: One subgroup gains up to 10 BMI points with higher Vitamin A intake, while the other subgroup loses up to 10 BMI points.

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