ArticleFrontiers in microbiology2022
LGBMDF: A cascade forest framework with LightGBM for predicting drug-target interactions.
Article in Frontiers in microbiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- [Prediction and verification of therapeutic drugs for triple-negative breast cancer using a knowledge graph-based drug repurposing model].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2026Article
- Article
- Prediction of miRNA-disease association based on multisource inductive matrix completion.Scientific reports · 2024Article
- Drug-target interaction prediction through fine-grained selection and bidirectional random walk methodology.Scientific reports · 2024Article
- Machine learning and radiomics for predicting efficacy of programmed cell death protein 1 inhibitor for small cell lung cancer: A multicenter cohort study.Clinical and translational medicine · 2024Article
- Machine learning to predict the occurrence of thyroid nodules: towards a quantitative approach for judicious utilization of thyroid ultrasonography.Frontiers in endocrinology · 2024Article
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Authors and funding
5 authors.
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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.
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