ArticleBriefings in bioinformatics2024
TargetCLP: clathrin proteins prediction combining transformed and evolutionary scale modeling-based multi-view features via weighted feature integration approach.
Article in Briefings in bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- DeepIR-Pred: identification of insulin receptors using metaheuristic optimization of biologically informed multi-view features with deep recurrent learning.Briefings in bioinformatics · 2026Article
- ProVenTL: a transfer-learning framework for predicting peptide-protein interactions derived from snake venom for cancer therapeutics.Journal of computer-aided molecular design · 2026Article
- Enhancing strawberry maturity assessment using mid-infrared spectral analysis with advanced variable selection and supervised classification.Scientific reports · 2026Article
- DeepBCTPred: deep learning-based prediction of bladder cancer tissues from endoscopic images.Bioinformatics advances · 2026Article
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- An Explainable Deep Learning Model for Clathrin Protein Prediction Using a DCT-Enhanced Position-Specific Scoring Matrix.Current drug targets · 2026Article
- Identifying protein succinylation sites using generative transformer and a two-dimensional representation with a deep capsule network.iScience · 2025Article
- pCPPs-sADNN: predicting cell-penetrating peptides using self-attention based deep neural network.Scientific reports · 2025Article
- An ensemble-based model comprising deep learning for predicting peptide-binding residues in proteins.NAR genomics and bioinformatics · 2025Article
- Deep‑learning based osteoporosis classification in knee X‑rays using transfer‑learning approach.Scientific reports · 2025Article
- M3Site: multiclass multimodal learning for protein active site identification and classification.Briefings in bioinformatics · 2025Article
- DeepBBMC biology · 2025Article
- BGATT-GR: accurate identification of glucocorticoid receptor antagonists based on data augmentation combined with BiGRU-attention.Scientific reports · 2025Article
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- Long sequence temporal knowledge tracing for student performance prediction via integrating LSTM and informer.PloS one · 2025Article
- An optimized transformer model for efficient detection of thoracic diseases in chest X-rays with multi-scale feature fusion.PloS one · 2025Article
- NFEmbed: modeling nitrogenase activity via classification and regression with pretrained protein embeddings.Bioinformatics advances · 2025Article
- K-M LLM-pro: Physics-guided cross-modal adaptation for fine-grained spatiotemporal trajectory classification.PloS one · 2025Article
Corrections and comments
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Authors and funding
5 authors.
Funding
Abstract
Clathrin proteins, key elements of the vesicle coat, play a crucial role in various cellular processes, including neural function, signal transduction, and endocytosis. Disruptions in clathrin protein functions have been associated with a wide range of diseases, such as Alzheimer's, neurodegeneration, viral infection, and cancer. Therefore, correctly identifying clathrin protein functions is critical to unravel the mechanism of these fatal diseases and designing drug targets. This paper presents a novel computational method, named TargetCLP, to precisely identify clathrin proteins. TargetCLP leverages four single-view feature representation methods, including two transformed feature sets (PSSM-CLBP and RECM-CLBP), one qualitative characteristics feature, and one deep-learned-based embedding using ESM. The single-view features are integrated based on their weights using differential evolution, and the BTG feature selection algorithm is utilized to generate a more optimal and reduced subset. The model is trained using various classifiers, among which the proposed SnBiLSTM achieved remarkable performance. Experimental and comparative results on both training and independent datasets show that the proposed TargetCLP offers significant improvements in terms of both prediction accuracy and generalization to unseen data, furthering advancements in the research field.
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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.