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Predictive Transcriptome Analysis

Instead of focusing on just one hypothesis about a specific disease, Gordian’s Pythia examines the entire transcriptome (the complete set of RNA molecules expressed from the Patient Avatar’s genes) to see how potential therapies affect various cellular pathways.

This allows Gordian to observe changes across multiple areas of cellular activity, providing a broader understanding of the effects of these therapies.

In other words, with most predictive transcriptome analysis, drug developers must decide in advance what they want to measure. With Pythia, we can be open-minded.

Pythia predicts how therapies will work in clinical (human) trials by combining transcriptomics data from Mosaic Screening with measurements of real-life clinical outcomes.

We use samples from human patients to make paired datasets that link the transcriptome to clinically relevant readouts. This lets us translate transcriptomic data from Mosaic Screening to predictions about physiological outcomes.

Pythia lets us analyze and understand various effects of the potential therapies, including information about the biological characteristics of the screened cells and similarities among disease areas.

As a result, each screening process we conduct enhances our ability to predict the effectiveness of potential treatments in future assessments.

Gene therapy allows us to write arbitrary changes to the genome in living cells. Single cell sequencing allows us to read the effects of those changes at the cellular level. Machine learning allows us to relate these cellular changes to clinically relevant outcomes for the whole organism. Together, these read / write / learn technologies allow us to manipulate and understand the genetic code driving the disease of interest in a living animal, and directly discover therapeutics that will modify that code and restore healthy function.

Ian Driver Head of Bioinformatics
A portrait of Ian Driver.