Improving Drug Sensitivity Prediction and Inference by Multitask Learning
Strauch, J.; Asiaee, A.
Show abstract
The development of models to predict sensitivity to anticancer drugs is an area of significant interest, given the diverse responses to treatment among patients and the considerable expense and time involved in anticancer drug development. Leveraging "omic" data and anticancer response information from the Cancer Cell Line Encyclopedia, we propose a novel approach utilizing multitask learning to enhance prediction accuracy and inference. We extended a multitask learning framework called the Data Shared Lasso to develop the Data Shared Elastic Net. This enabled the construction of tissue-specific models with information sharing while maintaining the attractive properties of Elastic Net regression. By employing this approach, we observed improvements in prediction accuracy compared to single-task Elastic Net models, particularly for cell lines displaying high sensitivity to treatment. Furthermore, the Data Shared Elastic Net facilitated the identification of predictors for anticancer drug sensitivity within specific tissue types, shedding light on cellular pathways targeted by these drugs across tissues. We also investigated the impact of data leakage on modeling outcomes from previous studies, which led to underestimating prediction error and erroneous inferences
Matching journals
The top 6 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Towards explainable interaction prediction: Embedding biological hierarchies into hyperbolic interaction space 96%
- Two-step multi-omics modelling of drug sensitivity in cancer cell lines to identify driving mechanisms 96%
- A cautionary tale about properly vetting datasets used in supervised learning predicting metabolic pathway involvement 95%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.