1. A Stacked Ensemble Approach For Enhancing Anti Cancer Drug Synergy Prediction.
- Author
-
Hafsath, C.A. and Jereesh, A.S.
- Subjects
DRUG synergism ,MACHINE learning ,ANTINEOPLASTIC agents ,RANDOM forest algorithms ,LOGISTIC regression analysis - Abstract
Combinations of drugs have demonstrated potential therapeutic results in controlling the progression of cancer, with a possible decrease in toxicity and unfavorable side efects. Experimental screening is no longer feasible due to the vast number of possible medication combinations. As a result, the scientific community is becoming more and more interested in creating computational models that can quickly and precisely identify prospective drug combinations. Our methodology employs therapeutically significant drugs and cell line properties in machine learning techniques. Our suggested stacked machine learning model outperforms all other machine learning models taken into consideration. It employs Random Forest and XGBoost as base learners and Logistic Regression as a meta learner. The results of our research underscore the remarkable efficacy of our approach. Not only does it address the complexities of the task at hand, but it also showcases its potential to enhance predictive accuracy within this domain. The comparative analysis reveals that our model exhibits a noteworthy performance improvement across multiple evaluation metrics. Our findings represent a significant step forward in the quest for improved cancer treatment strategies. [ABSTRACT FROM AUTHOR]
- Published
- 2024
- Full Text
- View/download PDF