LGBMDF: A Cascade Forest Framework with LightGBM for Predicting Drug-target Interactions

Overview

Journal Front Microbiol

Specialty Microbiology

Date 2023 Jan 23

PMID 36687573

Authors

Yu Peng

Shouwei Zhao

Zhiliang Zeng

Xiang Hu

Zhixiang Yin

Affiliations

Soon will be listed here.

Abstract

Prediction of drug-target interactions (DTIs) plays an important role in drug development. However, traditional laboratory methods to determine DTIs require a lot of time and capital costs. In recent years, many studies have shown that using machine learning methods to predict DTIs can speed up the drug development process and reduce capital costs. An excellent DTI prediction method should have both high prediction accuracy and low computational cost. In this study, we noticed that the previous research based on deep forests used XGBoost as the estimator in the cascade, we applied LightGBM instead of XGBoost to the cascade forest as the estimator, then the estimator group was determined experimentally as three LightGBMs and three ExtraTrees, this new model is called LGBMDF. We conducted 5-fold cross-validation on LGBMDF and other state-of-the-art methods using the same dataset, and compared their Sn, Sp, MCC, AUC and AUPR. Finally, we found that our method has better performance and faster calculation speed.

Citing Articles

ISLRWR: A network diffusion algorithm for drug-target interactions prediction.

Sun L, Yin Z, Lu L PLoS One. 2025; 20(1):e0302281.

PMID: 39883675 PMC: 11781719. DOI: 10.1371/journal.pone.0302281.

Prediction of miRNA-disease association based on multisource inductive matrix completion.

Wang Y, Yin Z Sci Rep. 2024; 14(1):27503.

PMID: 39528650 PMC: 11555322. DOI: 10.1038/s41598-024-78212-w.

Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.

Wang Y, Yin Z Sci Rep. 2024; 14(1):18104.

PMID: 39103483 PMC: 11300600. DOI: 10.1038/s41598-024-69186-w.

Machine learning and radiomics for predicting efficacy of programmed cell death protein 1 inhibitor for small cell lung cancer: A multicenter cohort study.

Li P, Huang L, Han R, Tang M, Fei G, Zeng D Clin Transl Med. 2024; 14(6):e1673.

PMID: 38840331 PMC: 11154803. DOI: 10.1002/ctm2.1673.

Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.

Liang Q, Qi Z, Li Y Front Endocrinol (Lausanne). 2024; 15:1385836.

PMID: 38774231 PMC: 11106422. DOI: 10.3389/fendo.2024.1385836.

References

Peng L, Wang C, Tian X, Zhou L, Li K . Finding lncRNA-Protein Interactions Based on Deep Learning With Dual-Net Neural Architecture. IEEE/ACM Trans Comput Biol Bioinform. 2021; 19(6):3456-3468. DOI: 10.1109/TCBB.2021.3116232. View

Law V, Knox C, Djoumbou Y, Jewison T, Guo A, Liu Y . DrugBank 4.0: shedding new light on drug metabolism. Nucleic Acids Res. 2013; 42(Database issue):D1091-7. PMC: 3965102. DOI: 10.1093/nar/gkt1068. View

Pu Y, Li J, Tang J, Guo F . DeepFusionDTA: Drug-Target Binding Affinity Prediction With Information Fusion and Hybrid Deep-Learning Ensemble Model. IEEE/ACM Trans Comput Biol Bioinform. 2021; 19(5):2760-2769. DOI: 10.1109/TCBB.2021.3103966. View

An Q, Yu L . A heterogeneous network embedding framework for predicting similarity-based drug-target interactions. Brief Bioinform. 2021; 22(6). DOI: 10.1093/bib/bbab275. View

Cheng F, Lu W, Liu C, Fang J, Hou Y, Handy D . A genome-wide positioning systems network algorithm for in silico drug repurposing. Nat Commun. 2019; 10(1):3476. PMC: 6677722. DOI: 10.1038/s41467-019-10744-6. View

Lin W, Wu L, Zhang Y, Wen Y, Yan B, Dai C . An enhanced cascade-based deep forest model for drug combination prediction. Brief Bioinform. 2022; 23(2). DOI: 10.1093/bib/bbab562. View

Pliakos K, Vens C, Tsoumakas G . Predicting Drug-Target Interactions With Multi-Label Classification and Label Partitioning. IEEE/ACM Trans Comput Biol Bioinform. 2019; 18(4):1596-1607. DOI: 10.1109/TCBB.2019.2951378. View

Zeng X, Zhu S, Lu W, Liu Z, Huang J, Zhou Y . Target identification among known drugs by deep learning from heterogeneous networks. Chem Sci. 2021; 11(7):1775-1797. PMC: 8150105. DOI: 10.1039/c9sc04336e. View

Gaulton A, Bellis L, Bento A, Chambers J, Davies M, Hersey A . ChEMBL: a large-scale bioactivity database for drug discovery. Nucleic Acids Res. 2011; 40(Database issue):D1100-7. PMC: 3245175. DOI: 10.1093/nar/gkr777. View

10.

Zhan X, You Z, Yu C, Li L, Pan J . Ensemble Learning Prediction of Drug-Target Interactions Using GIST Descriptor Extracted from PSSM-Based Evolutionary Information. Biomed Res Int. 2020; 2020:4516250. PMC: 7463380. DOI: 10.1155/2020/4516250. View

11.

Mei J, Kwoh C, Yang P, Li X, Zheng J . Drug-target interaction prediction by learning from local information and neighbors. Bioinformatics. 2012; 29(2):238-45. DOI: 10.1093/bioinformatics/bts670. View

12.

Liu T, Lin Y, Wen X, Jorissen R, Gilson M . BindingDB: a web-accessible database of experimentally determined protein-ligand binding affinities. Nucleic Acids Res. 2006; 35(Database issue):D198-201. PMC: 1751547. DOI: 10.1093/nar/gkl999. View

13.

Hernandez-Boussard T, Whirl-Carrillo M, Hebert J, Gong L, Owen R, Gong M . The pharmacogenetics and pharmacogenomics knowledge base: accentuating the knowledge. Nucleic Acids Res. 2007; 36(Database issue):D913-8. PMC: 2238877. DOI: 10.1093/nar/gkm1009. View

14.

Wishart D, Feunang Y, Guo A, Lo E, Marcu A, Grant J . DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2017; 46(D1):D1074-D1082. PMC: 5753335. DOI: 10.1093/nar/gkx1037. View

15.

Chen C, Shi H, Jiang Z, Salhi A, Chen R, Cui X . DNN-DTIs: Improved drug-target interactions prediction using XGBoost feature selection and deep neural network. Comput Biol Med. 2021; 136:104676. DOI: 10.1016/j.compbiomed.2021.104676. View

16.

Sajadi S, Chahooki M, Gharaghani S, Abbasi K . AutoDTI++: deep unsupervised learning for DTI prediction by autoencoders. BMC Bioinformatics. 2021; 22(1):204. PMC: 8056558. DOI: 10.1186/s12859-021-04127-2. View

17.

Jarada T, Rokne J, Alhajj R . SNF-NN: computational method to predict drug-disease interactions using similarity network fusion and neural networks. BMC Bioinformatics. 2021; 22(1):28. PMC: 7821180. DOI: 10.1186/s12859-020-03950-3. View

18.

Yuan Q, Gao J, Wu D, Zhang S, Mamitsuka H, Zhu S . DrugE-Rank: improving drug-target interaction prediction of new candidate drugs or targets by ensemble learning to rank. Bioinformatics. 2016; 32(12):i18-i27. PMC: 4908328. DOI: 10.1093/bioinformatics/btw244. View

19.

Shen L, Liu F, Huang L, Liu G, Zhou L, Peng L . VDA-RWLRLS: An anti-SARS-CoV-2 drug prioritizing framework combining an unbalanced bi-random walk and Laplacian regularized least squares. Comput Biol Med. 2021; 140:105119. PMC: 8664497. DOI: 10.1016/j.compbiomed.2021.105119. View

20.

Bagherian M, Kim R, Jiang C, Sartor M, Derksen H, Najarian K . Coupled matrix-matrix and coupled tensor-matrix completion methods for predicting drug-target interactions. Brief Bioinform. 2020; 22(2):2161-2171. PMC: 7986629. DOI: 10.1093/bib/bbaa025. View