ArticleScientific reports2017
iDTI-ESBoost: Identification of Drug Target Interaction Using Evolutionary and Structural Features with Boosting.
Article in Scientific reports, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 36 papers.
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36 citing papers in PubMed, 5 citations in OpenAlex.
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- Small data, big challenges: Machine- and deep-learning strategies for data-limited drug discovery.Advanced drug delivery reviews · 2026Review
- In silico methods for drug-target interaction prediction.Cell reports methods · 2025Review
- QKDTI A quantum kernel based machine learning model for drug target interaction prediction.Scientific reports · 2025Article
- Prediction of drug target interaction based on under sampling strategy and random forest algorithm.PloS one · 2025Article
- Next-generation non-animal models for inflammatory bowel disease:NAM journal · 2025Review
- Predicting drug protein interactions based on improved support vector data description in unbalanced data.BioImpacts : BI · 2025Article
- DAE-CFR: detecting microRNA-disease associations using deep autoencoder and combined feature representation.BMC bioinformatics · 2024Article
- Advancing drug-target interaction prediction: a comprehensive graph-based approach integrating knowledge graph embedding and ProtBert pretraining.BMC bioinformatics · 2023Article
- Article
- SSELM-neg: spherical search-based extreme learning machine for drug-target interaction prediction.BMC bioinformatics · 2023Article
- Modern drug discovery for inflammatory bowel disease: The role of computational methods.World journal of gastroenterology · 2023Review
- LM-DTI: a tool of predicting drug-target interactions using the node2vec and network path score methods.Frontiers in genetics · 2023Article
- Article
- AttentionSiteDTI: an interpretable graph-based model for drug-target interaction prediction using NLP sentence-level relation classification.Briefings in bioinformatics · 2022Article
- DeepStack-DTIs: Predicting Drug-Target Interactions Using LightGBM Feature Selection and Deep-Stacked Ensemble Classifier.Interdisciplinary sciences, computational life sciences · 2022Article
- UnbiasedDTI: Mitigating Real-World Bias of Drug-Target Interaction Prediction by Using Deep Ensemble-Balanced Learning.Molecules (Basel, Switzerland) · 2022Article
- Drug-target interaction prediction via multiple classification strategies.BMC bioinformatics · 2022Article
- A clustering-based sampling method for miRNA-disease association prediction.Frontiers in genetics · 2022Article
- The Discovery of New Drug-Target Interactions for Breast Cancer Treatment.Molecules (Basel, Switzerland) · 2021Article
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Authors and funding
7 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Prediction of new drug-target interactions is critically important as it can lead the researchers to find new uses for old drugs and to disclose their therapeutic profiles or side effects. However, experimental prediction of drug-target interactions is expensive and time-consuming. As a result, computational methods for predictioning new drug-target interactions have gained a tremendous interest in recent times. Here we present iDTI-ESBoost, a prediction model for identification of drug-target interactions using evolutionary and structural features. Our proposed method uses a novel data balancing and boosting technique to predict drug-target interaction. On four benchmark datasets taken from a gold standard data, iDTI-ESBoost outperforms the state-of-the-art methods in terms of area under receiver operating characteristic (auROC) curve. iDTI-ESBoost also outperforms the latest and the best-performing method found in the literature in terms of area under precision recall (auPR) curve. This is significant as auPR curves are argued as suitable metric for comparison for imbalanced datasets similar to the one studied here. Our reported results show the effectiveness of the classifier, balancing methods and the novel features incorporated in iDTI-ESBoost. iDTI-ESBoost is a novel prediction method that has for the first time exploited the structural features along with the evolutionary features to predict drug-protein interactions. We believe the excellent performance of iDTI-ESBoost both in terms of auROC and auPR would motivate the researchers and practitioners to use it to predict drug-target interactions. To facilitate that, iDTI-ESBoost is implemented and made publicly available at: http://farshidrayhan.pythonanywhere.com/iDTI-ESBoost/ .
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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.