I developed a machine learning model for the classification of senescent cells based on their cell morphology - SAMP-Score.
SAMP-Score was built and validated through analysis of high-throughput screening data and multiparameter phenotypic analysis of the following large scale data sets:
- Genome-wide siRNA (x63 384-well plates – 24,122 treatments).
- Compound diversity library (x61 384-well plates – 20,000 treatments).
- Hit validation dose response (x30 384-well plates – 8,940 treatments).
SAMP-Score has been used to identify novel compounds that initiate a senescence response in cancer cells, demonstrating its utility as a drug discovery tool.
ML Techniques: Regression (Logistic), regularisation (Lasso, Elastic Net), support vector machines (SVM), decision trees (Random Forrest), dimensionality reduction (MDA), neural networks, ensemble model stacking and hierarchical clustering
