ArticleFrontiers in genetics2019
Gradient Boosting Decision Tree-Based Method for Predicting Interactions Between Target Genes and Drugs.
Article in Frontiers in genetics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
36 citing papers in PubMed.
- SR2P: an efficient stacking method to predict protein abundance from gene expression in spatial transcriptomics data.bioRxiv : the preprint server for biology · 2026Article
- Graph based link prediction for epilepsy drug discovery.Scientific reports · 2025Article
- Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN.BMC bioinformatics · 2025Article
- Interpretable machine learning for depression recognition with spatiotemporal gait features among older adults: a cross-sectional study in Xiamen, China.BMC geriatrics · 2025Article
- Utilizing SMOTE-TomekLink and machine learning to construct a predictive model for elderly medical and daily care services demand.Scientific reports · 2025Article
- Enhancing drug repositioning: A multi-class ensemble model for drug-target interaction prediction with action type categorization.PloS one · 2025Article
- Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review.Frontiers in bioengineering and biotechnology · 2025Review
- MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.Briefings in bioinformatics · 2024Article
- Mining Chemogenomic Spaces for Prediction of Drug-Target Interactions.Methods in molecular biology (Clifton, N.J.) · 2024Article
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing · 2024Article
- Comprehensive Review on Drug-target Interaction Prediction - Latest Developments and Overview.Current drug discovery technologies · 2024Review
- TeM-DTBA: time-efficient drug target binding affinity prediction using multiple modalities with Lasso feature selection.Journal of computer-aided molecular design · 2023Article
- Machine Learning Strategies for Improved Phenotype Prediction in Underrepresented Populations.bioRxiv : the preprint server for biology · 2023Article
- EDST: a decision stump based ensemble algorithm for synergistic drug combination prediction.BMC bioinformatics · 2023Review
- LEGO-CSM: a tool for functional characterization of proteins.Bioinformatics (Oxford, England) · 2023Article
- Employing active learning in the optimization of culture medium for mammalian cells.NPJ systems biology and applications · 2023Article
- Multiclass characterization of frontotemporal dementia variants via multimodal brain network computational inference.Network neuroscience (Cambridge, Mass.) · 2023Article
- LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.Frontiers in genetics · 2023Article
- A hybrid machine learning model of depression estimation in home-based older adults: a 7-year follow-up study.BMC psychiatry · 2022Article
- Multi-feature computational framework for combined signatures of dementia in underrepresented settings.Journal of neural engineering · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
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
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.