Our specialists combine discriminating features from rich spectral data with demographic and phenotypic information to create robust classification algorithms.
We identify and extract clinically-relevant features from the chemical spectra data to classify patients by disease state.
Our highly skilled data scientist team takes the output data from sample analysis and processes it through compression techniques such as discrete wavelet transforms or principal component analysis to aid in feature discovery.
These identified features are combined with patient clinical and outcome data from our proprietary database to develop biomarker classification methods. The form of the classifier will depend on the dataset, but random forest, sparse logistic regression, and support vector machines have all been successfully deployed in biomarker discovery.
We generate standard statistical outputs including principal component analysis, box plots and receiver operating characteristics curves (ROC), showing the range of concentration for given patient sub-populations demonstrating the predictive power of each potential biomarker and the overall performance of the classification algorithms.
Our classification algorithms use multiple biomarkers to accurately identify patients by disease presence, therapy response or outcome.
Using patient history, comorbidity and outcome information along with biomarker concentration data we create tests to make actionable patient classifications that can be deployed in clinic. By generating patient specific probabilities for disease states you gain detailed insights into test performance as well as the means to optimize the positive and negative predictive values of the test to match your application.