ArticleInternational journal of molecular sciences2021
Prediction of Protein-ATP Binding Residues Based on Ensemble of Deep Convolutional Neural Networks and LightGBM Algorithm.
Article in International journal of molecular sciences, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Protein Science Meets Artificial Intelligence: A Systematic Review and a Biochemical Meta-Analysis of an Inter-Field.Frontiers in bioengineering and biotechnology · 2022Pooled it
- A Novel Weighted Ensemble Framework of Transformer and Deep Q-Network for ATP-Binding Site Prediction Using Protein Language Model Features.International journal of molecular sciences · 2026Article
- Accurate prediction of protein-ATP binding sites based on a protein pretrained large language model and a fractional-order convolutional neural network.Scientific reports · 2025Article
- Predicting the binding residues of four small molecule ligands by utilizing ensemble algorithms with additional correlation features.Scientific reports · 2025Article
- Automated drug design for druggable target identification using integrated stacked autoencoder and hierarchically self-adaptive optimization.Scientific reports · 2025Article
- MultiToxPred 1.0: a novel comprehensive tool for predicting 27 classes of protein toxins using an ensemble machine learning approach.BMC bioinformatics · 2024Article
- A comprehensive review of protein-centric predictors for biomolecular interactions: from proteins to nucleic acids and beyond.Briefings in bioinformatics · 2024Review
- Proteomic advance of ischemic stroke: preclinical, clinical, and intervention.Metabolic brain disease · 2023Review
- Integrating Structured and Unstructured EHR Data for Predicting Mortality by Machine Learning and Latent Dirichlet Allocation Method.International journal of environmental research and public health · 2023Article
- Applying an Improved Stacking Ensemble Model to Predict the Mortality of ICU Patients with Heart Failure.Journal of clinical medicine · 2022Article
- A Presurgical Unfavorable Prediction Scale of Endovascular Treatment for Acute Ischemic Stroke.Frontiers in aging neuroscience · 2022Article
- Hybrid Deep Learning Models with Sparse Enhancement Technique for Detection of Newly Grown Tree Leaves.Sensors (Basel, Switzerland) · 2021Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Accurately identifying protein-ATP binding residues is important for protein function annotation and drug design. Previous studies have used classic machine-learning algorithms like support vector machine (SVM) and random forest to predict protein-ATP binding residues; however, as new machine-learning techniques are being developed, the prediction performance could be further improved. In this paper, an ensemble predictor that combines deep convolutional neural network and LightGBM with ensemble learning algorithm is proposed. Three subclassifiers have been developed, including a multi-incepResNet-based predictor, a multi-Xception-based predictor, and a LightGBM predictor. The final prediction result is the combination of outputs from three subclassifiers with optimized weight distribution. We examined the performance of our proposed predictor using two datasets: a classic ATP-binding benchmark dataset and a newly proposed ATP-binding dataset. Our predictor achieved area under the curve (AUC) values of 0.925 and 0.902 and Matthews Correlation Coefficient (MCC) values of 0.639 and 0.642, respectively, which are both better than other state-of-art prediction methods.
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Registered trials
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