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Cited by 101Computational Drug Discovery MethodsBioinformatics and Genomic NetworksMachine Learning in Bioinformatics

The DOI matches its Crossref record; 78 of the 78 references it cites were found in a registry or an index.

Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs

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Xuan, Ping, Sun, Chang, Zhang, Tiangang, Ye, Yilin, Shen, Tonghui, Dong, Yihua·2019·Frontiers in Genetics
DOI 10.3389/fgene.2019.00459PMC PMC6555260
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Abstract

Determining the target genes that interact with drugs—drug–target interactions—plays an important role in drug discovery. Identification of drug–target interactions through biological experiments is time consuming, laborious, and costly. Therefore, using computational approaches to predict candidate targets is a good way to reduce the cost of wet-lab experiments. However, the known interactions (positive samples) and the unknown interactions (negative samples) display a serious class imbalance, which has an adverse effect on the accuracy of the prediction results. To mitigate the impact of class imbalance and completely exploit the negative samples, we proposed a new method, named DTIGBDT, based on gradient boosting decision trees, for predicting candidate drug–target interactions. We constructed a drug–target heterogeneous network that contains the drug similarities based on the chemical structures of drugs, the target similarities based on target sequences, and the known drug–target interactions. The topological information of the network was captured by random walks to update the similarities between drugs or targets. The paths between drugs and targets could be divided into multiple categories, and the features of each category of paths were extracted. We constructed a prediction model based on gradient boosting decision trees. The model establishes multiple decision trees with the extracted features and obtains the interaction scores between drugs and targets. DTIGBDT is a method of ensemble learning, and it effectively reduces the impact of class imbalance. The experimental results indicate that DTIGBDT outperforms several state-of-the-art methods for drug–target interaction prediction. In addition, case studies on Quetiapine, Clozapine, Olanzapine, Aripiprazole, and Ziprasidone demonstrate the ability of DTIGBDT to discover potential drug–target interactions.

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Crossref · 10.3389/fgene.2019.00459

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Figure 1 Algorithm flow of DTIGBDT. (A) Construct the heterogeneous network. (B) Random walk on drug network and target network, respectively. (C) Select most similar k neighbors. (D) Get feature vectors for each drug–target pair. (E) Train the DTIGBDT with the feature vectors.
Figure 1 Algorithm flow of DTIGBDT. (A) Construct the heterogeneous network. (B) Random walk on drug network and target network, respectively. (C) Select most similar k neighbors. (D) Get feature vectors for each drug–target pair. (E) Train the DTIGBDT with the feature vectors.CC BY · PubMed Central
Figure 2 Feature vector calculation of d7-t3. The edges between drug nodes or target nodes are weighted by the similarities between two nodes. The edges between drugs and target nodes represent the known DTIs and are weighted by 1. (A) Paths between d7 and t3. (B) The s-values of all the paths. (C) Three types of path feature vectors. (D) Connection of three feature vectors.
Figure 2 Feature vector calculation of d7-t3. The edges between drug nodes or target nodes are weighted by the similarities between two nodes. The edges between drugs and target nodes represent the known DTIs and are weighted by 1. (A) Paths between d7 and t3. (B) The s-values of all the paths. (C) Three types of path feature vectors. (D) Connection of three feature vectors.CC BY · PubMed Central
Figure 3 Algorithm for predicting the potential drug–target interactions.
Figure 3 Algorithm for predicting the potential drug–target interactions.CC BY · PubMed Central
Figure 4 ROC curves and precision–recall curves of DTI prediction by different methods.
Figure 4 ROC curves and precision–recall curves of DTI prediction by different methods.CC BY · PubMed Central
Figure 5 The average recalls across all the tested drugs at different top k-values.
Figure 5 The average recalls across all the tested drugs at different top k-values.CC BY · PubMed Central

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Metadata

Authors
Xuan, P., Sun, C., Zhang, T., Ye, Y., Shen, T., & Dong, Y.
Affiliations
Heilongjiang University (CN)
Corresponding author
  • Sun, Chang
  • Zhang, Tiangang
ORCID
Venue
Frontiers in Genetics
Publisher
Frontiers Media SA
Published
2019-05-31
Volume
10
Pages
459-459
ISSN
1664-8021 (ISSN-L)
DOI
10.3389/fgene.2019.00459
PMC
PMC6555260
OpenAlex
W2946853220
Cited by
100
References
78
Citations by year
2026: 7 · 2025: 16 · 2024: 8 · 2023: 19 · 2022: 18 · 2021: 17 · 2020: 11 · 2019: 4
FWCI
8.50
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Genetics (publishedVersion, cc-by)
  • Europe PMC (PubMed Central) (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (61702296, 61302139)
  • Natural Science Foundation of Heilongjiang Province (LH2019F049, LH2019A029)
Topics
  • Life Sciences › Pharmacology, Toxicology and Pharmaceutics › Drug Discovery › Computational Drug Discovery Methods
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
  • Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Machine Learning in Bioinformatics
Keywords
drug-target interaction prediction, gradient boosting decision tree, class imbalance, random walks, ensemble learning, antipsychotic drugs, drug discovery

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