EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction

There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine...

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Bibliographic Details
Published in:BMC bioinformatics 2023-08, Vol.24 (1), p.1-325, Article 325
Main Authors: Chen, Jing, Wu, Lianlian, Liu, Kunhong, Xu, Yong, He, Song, Bo, Xiaochen
Format: Article
Language:English
Subjects:
Accuracy
Algorithms
Analysis
Bagging
Bioinformatics
Cell lines
Classification
Clinical trials
Data mining
Datasets
Decision making
Decision stump
Decision tree
Decision trees
Deep learning
Disease
Drug combination
Drug resistance
Drug therapy, Combination
Interpretability
Machine learning
Methods
Performance evaluation
Pharmaceutical research
Predictions
Probability
Synergistic effect
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Summary:There are countless possibilities for drug combinations, which makes it expensive and time-consuming to rely solely on clinical trials to determine the effects of each possible drug combination. In order to screen out the most effective drug combinations more quickly, scholars began to apply machine learning to drug combination prediction. However, most of them are of low interpretability. Consequently, even though they can sometimes produce high prediction accuracy, experts in the medical and biological fields can still not fully rely on their judgments because of the lack of knowledge about the decision-making process. This study proposes a decision stump (DS)-based solution to extract interpretable knowledge from data sets. In this method, a set of DSs is first generated to selectively form a decision tree (DST). Different from the traditional decision tree, our algorithm not only enables a partial exchange of information between base classifiers by introducing a stump exchange method but also uses a modified Gini index to evaluate stump performance so that the generation of each node is evaluated by a global view to maintain high generalization ability. Furthermore, these trees are combined to construct an ensemble of DST (EDST). The novel decision tree ensemble model can effectively predict the effect of drug combination datasets and easily obtain the decision-making process.
ISSN:1471-2105
1471-2105
DOI:10.1186/s12859-023-05453-3