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Application Of Transfer Learning For Cancer Drug Sensitivity Prediction, Saugato Rahman Dhruba, Raziur Rahman, Kevin Matlock, Souparno Ghosh, Ranadip Pal
Application Of Transfer Learning For Cancer Drug Sensitivity Prediction, Saugato Rahman Dhruba, Raziur Rahman, Kevin Matlock, Souparno Ghosh, Ranadip Pal
Department of Statistics: Faculty Publications
Background: In precision medicine, scarcity of suitable biological data often hinders the design of an appropriate predictive model. In this regard, large scale pharmacogenomics studies, like CCLE and GDSC hold the promise to mitigate the issue. However, one cannot directly employ data from multiple sources together due to the existing distribution shift in data. One way to solve this problem is to utilize the transfer learning methodologies tailored to fit in this specific context.
Results: In this paper, we present two novel approaches for incorporating information from a secondary database for improving the prediction in a target database. The first …
Investigation Of Model Stacking For Drug Sensitivity Prediction, Kevin Matlock, Carlos De Niz, Raziur Rahman, Souparno Ghosh, Ranadip Pal
Investigation Of Model Stacking For Drug Sensitivity Prediction, Kevin Matlock, Carlos De Niz, Raziur Rahman, Souparno Ghosh, Ranadip Pal
Department of Statistics: Faculty Publications
Background: A significant problem in precision medicine is the prediction of drug sensitivity for individual cancer cell lines. Predictive models such as Random Forests have shown promising performance while predicting from individual genomic features such as gene expressions. However, accessibility of various other forms of data types including information on multiple tested drugs necessitates the examination of designing predictive models incorporating the various data types.
Results: We explore the predictive performance of model stacking and the effect of stacking on the predictive bias and squarred error. In addition we discuss the analytical underpinnings supporting the advantages of stacking in reducing …