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
Genteract automatically creates diagnostics which predict which patient subgroups will experience a clinically significant response, with acceptable side effects ... More
Genteract CVGxE creates diagnostics that optimize population targeting, allowing patients to be matched with the drugs that will work best for them... aggrade
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 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 patientâs response to the drug.
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.
Highly increased statistical power over previous methods, enabling more accurate predictions using smaller datasets.
Our methodology interprets continuous phenotype and environmental variables, rather than relying on case-control logic that is not appropriate for most complex traits.
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.
Genteract Analysis supports genotype data, sequencing data, time-series data, and can work with standard or non-standard formats.
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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