Machine Learning for Precision Medicine
Machine learning is a discipline within the field of artificial intelligence (AI) where algorithms are applied iteratively to a large data set in order to automate analytical model building. With each iteration, the algorithm “learns” from the data and its performance is improved.
In precision medicine, machine learning can be applied to data repositories too large for the human brain to parse. The patterns found in those large data repositories can help researchers to draw conclusions and predict an event.
Machine learning applications can help understand clinical variables and/or molecular properties to predict:
- Disease onset, status, and relapse
- Efficacy and safety profiles of a treatment
- Other patient characteristics
BSSI has utilized machine learning approaches in many projects:
- Drug response prediction using DNA or RNA data and identify patient subgroups with improved treatment effect
- Prediction of disease diagnosis or relapse after treatment using biomarker data for developing potential companion diagnostics or other diagnostic tests
- Support of clients in presenting machine learning and predictive modeling results to regulatory agencies