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ONCOLOGY CLINICAL TRIALS ORGANIZATIONS
Academic Research/Hospitals and Government Clinical Studies
Pharma, BioTech, and CRO’s (Contract Research Organizations)
ONCOLOGY CLINICAL RESEARCH
Academic Health Centers, Biotech & Pharma
Genetic Targeting of Cancer Drug Leads
Cell-line, Pre-clinical, and Animal Drug Studies
CANCER DRUG DEVELOPMENT
CANCER DRUG DEVELOPMENT
PHARMA, BIOTECH & ACADEMIC R&D
Lead Selection and Optimization (genetically targeted)
Repositioning drugs based on patient populations
GENOMICS AND BIOMARKER STUDIES
License our Pre-Analyzed Reference Databases (MatchTxBase)
Analysis and visualization of cancer genetic mutations of unknown significance
Novel biomarker discovery
Application of MatchTx Algorithms to other Diseases
BIOMARKERS AND GENOME RESEARCH
Using the MatchTx Solution
Matching Patients to Cancer Treatments
MatchTx changes the way cancer clinical trials are conducted. Our team of bioinformaticists has developed algorithms that create a personalized match between a novel treatment and those patients most likely to respond positively, making clinical trials faster, cheaper, and more effective.
The MatchTx technology is defined by a patent-pending method that combines genomic data and clinical outcome data in a single platform to match each individual patient to the best treatment using novel classification algorithms and a reference data set of genomic and outcome data.
Advanced Technology for Post-Analysis Integration
We use Clinical and ‘Omic’ data as patient keys. Find the best patient matches using the MatchTx algorithms, then infer outcome from the matches. After this analysis, determine which clinicogenomic features drive the outcome to identify and then validate the predictive models.
Match based data integration has many advantages:
Avoids curse of data dimensionality
Embraces shifting semantic representations
Reduces signal to noise
Permits data reuse
Simpler clinical data integration – simply match on existing features
We’re ready to partner with you.
Better understand your data
We integrate multiple kinds of clinical, ‘omics’ and drug information to enable you to identify key drivers of drug effectiveness and patient/sample outcomes where there are large numbers of variations and no dominant single variant predictors of outcome.
MatchTx models make no prior assumptions about data or biomarkers, and prove to be better predictors of research and clinical outcomes of therapeutics on similar patients/samples than alternative neural network or single biomarker approaches.