Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm

Mansour, Ayman

doi:10.19101/ijacr.2018.836007

Cited by 16 publications

(7 citation statements)

References 25 publications

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“…In addition, GA has been applied to solving complex medical problems, including disease screening, disease diagnosis, prognosis, and healthcare management . In drug discovery, GA has been used in evaluating adverse drug reactions, de novo drug design, and lead optimization. − …”

Section: Introductionmentioning

confidence: 99%

“…36 In addition, GA has been applied to solving complex medical problems, including disease screening, 37 disease diagnosis, 38 prognosis, 39 and healthcare management. 40 In drug discovery, GA has been used in evaluating adverse drug reactions, 41 de novo drug design, and lead optimization. 42−44 In this study, the drug combination prediction task is modeled as a three-class classification problem, which includes synergy, antagonism, and additive classes.…”

Section: ■ Introductionmentioning

confidence: 99%

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A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

Zhang³

et al. 2023

J. Chem. Inf. Model.

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Combination therapy is a promising clinical treatment strategy for cancer and other complex diseases. Multiple drugs can target multiple proteins and pathways, greatly improving the therapeutic effect and slowing down drug resistance. To narrow the search space of synergistic drug combinations, many prediction models have been developed. However, drug combination datasets always have the characteristics of class imbalance. Synergistic drug combinations receive the most attention in clinical application but are in small numbers. To predict synergistic drug combinations in different cancer cell lines, in this study, we propose a genetic algorithm-based ensemble learning framework, GA-DRUG, to address the problems of class imbalance and high dimensionality of input data. The cell-line-specific gene expression profiles under drug perturbations are used to train GA-DRUG, which contains imbalanced data processing and the search of global optimal solutions. Compared to 11 state-of-the-art algorithms, GA-DRUG achieves the best performance and significantly improves the prediction performance in the minority class (Synergy). The ensemble framework can effectively correct the classification results of a single classifier. In addition, the cellular proliferation experiment performed on several previously unexplored drug combinations further confirms the predictive ability of GA-DRUG.

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Section: Introductionmentioning

confidence: 99%

Section: ■ Introductionmentioning

confidence: 99%

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

Zhang³

et al. 2023

J. Chem. Inf. Model.

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“…GA has also been applied to solve complex medical problems, including disease screening [ 35 ], disease diagnosis [ 36 ], prognosis [ 37 ], and health care management [ 38 ]. In drug discovery, GA has been used in the evaluation of adverse drug reactions [ 39 ], drug design, and lead optimization [ 40 , 41 , 42 ].…”

Section: Introductionmentioning

confidence: 99%

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

Yan

Wang

et al. 2022

Molecules

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In the process of drug discovery, drug-induced liver injury (DILI) is still an active research field and is one of the most common and important issues in toxicity evaluation research. It directly leads to the high wear attrition of the drug. At present, there are a variety of computer algorithms based on molecular representations to predict DILI. It is found that a single molecular representation method is insufficient to complete the task of toxicity prediction, and multiple molecular fingerprint fusion methods have been used as model input. In order to solve the problem of high dimensional and unbalanced DILI prediction data, this paper integrates existing datasets and designs a new algorithm framework, Rotation-Ensemble-GA (R-E-GA). The main idea is to find a feature subset with better predictive performance after rotating the fusion vector of high-dimensional molecular representation in the feature space. Then, an Adaboost-type ensemble learning method is integrated into R-E-GA to improve the prediction accuracy. The experimental results show that the performance of R-E-GA is better than other state-of-art algorithms including ensemble learning-based and graph neural network-based methods. Through five-fold cross-validation, the R-E-GA obtains an ACC of 0.77, an F1 score of 0.769, and an AUC of 0.842.

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“…The main aspect of knowledge discovery is to analyse the knowledge in such a way to summarize the approaches in the similar way based on the data attributes, similarity behaviour and attribute properties [12].…”

Section: Introductionmentioning

confidence: 99%

An efficient ICKM approach for similarity measurement and distance estimation based on k-means

Kumari¹,

Sharma²

2020

IJATEE

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An iterative centroid initialization k-means (ICKM) based clustering has been proposed in this paper. In this approach first the dataset selection has been performed along with the option of choosing and selection as per the data use or the user can access partial data also based on the iterative centroid.Then the data preprocessing steps are followed for the data arrangement and analysis. There are four different distance algorithms have been considered with the k-means. These algorithms provide the complete variability for the distance estimation and production. The proposed method found to be useful along with different distance estimation and measures.

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Decision tree-based expert system for adverse drug reaction detection using fuzzy logic and genetic algorithm

Cited by 16 publications

References 25 publications

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

A Genetic Algorithm-Based Ensemble Learning Framework for Drug Combination Prediction

An Algorithm Framework for Drug-Induced Liver Injury Prediction Based on Genetic Algorithm and Ensemble Learning

An efficient ICKM approach for similarity measurement and distance estimation based on k-means

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