ArticleBMC bioinformatics2024
Deep-m5U: a deep learning-based approach for RNA 5-methyluridine modification prediction using optimized feature integration.
Article in BMC bioinformatics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed.
- Enhancing tumor T cell antigen prediction by integrating deep protein representations.BMC bioinformatics · 2026Article
- The Use of Deep Learning in RNA Therapeutic Development.ACS nano · 2026Review
- Deep learning-based identification of N6-methyladenine sites via hybrid feature fusion and SHAP-driven feature selection.Scientific reports · 2026Article
- Optimized deep intelligent parameter efficient fine-tuning protein language model system for predicting dephosphorylation site.Journal of molecular modeling · 2026Article
- Predicting circRNA subcellular localization by fusing circRNA sequence and network information.Scientific reports · 2026Article
- AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- BiToxNet: a deep learning framework integrating multimodal features for accurate identification of neurotoxic peptides and proteins.BMC biology · 2026Article
- Deep learning framework with interpretable feature selection for accurate SUMOylation site prediction.Scientific reports · 2026Article
- A universal indel filtering workflow for both long-read and short-read NGS data.BMC research notes · 2026Article
- AI-MDT: an automatic and intelligent multidisciplinary team consultations platform for lung cancer diagnosis.Journal of cancer research and clinical oncology · 2026Article
- The Rare RNA Methylations m²G, Cm, m⁵U and ms²i⁶A: Roles in Disease Pathogenesis and Emerging Therapeutic Implications.International journal of medical sciences · 2026Review
- Chronic liver disease classification using deep learning with SHAP-optimized hybrid features.iScience · 2025Article
- Comparative performance of bagging and boosting ensemble models for predicting lumpy skin disease with multiclass-imbalanced data.Scientific reports · 2025Article
- A robust deep learning framework for RNA 5-methyluridine modification prediction using integrated features.BMC biology · 2025Article
- AIPred: comprehensive prediction and analysis of non-histone acetylation via protein language model and interpretable machine learning.BMC biology · 2025Article
- Peripheral blood multimodal integration via cross-attention for cancer immune profiling.BMC cancer · 2025Article
- Review
- MultiFG: integrating molecular fingerprints and graph embeddings via attention mechanisms for robust drug side effect prediction.Scientific reports · 2025Article
- N6-methyladenine identification using deep learning and discriminative feature integration.BMC medical genomics · 2025Article
- pACP-HybDeep: predicting anticancer peptides using binary tree growth based transformer and structural feature encoding with deep-hybrid learning.Scientific reports · 2025Article
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7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRNA 5-methyluridine (m5U) modifications play a crucial role in biological processes, making their accurate identification a key focus in computational biology. This paper introduces Deep-m5U, a robust predictor designed to enhance the prediction of m5U modifications. The proposed method, named Deep-m5U, utilizes a hybrid pseudo-K-tuple nucleotide composition (PseKNC) for sequence formulation, a Shapley Additive exPlanations (SHAP) algorithm for discriminant feature selection, and a deep neural network (DNN) as the classifier.
resultsThe model was evaluated using two benchmark datasets, i.e., Full Transcript and Mature mRNA. Deep-m5U achieved overall accuracies of 91.47% and 95.86% for the Full Transcript and Mature mRNA datasets with 10-fold cross-validation, and for independent samples, the model attained 92.94% and 95.17% accuracy.
conclusionCompared to existing models, Deep-m5U showed approximately 5.23% and 3.73% higher accuracy on the training data and 3.95% and 3.26% higher accuracy on independent samples for the Full Transcript and Mature mRNA datasets, respectively. The reliability and effectiveness of Deep-m5U make it a valuable tool for scientists and a potential asset in pharmaceutical design and research.
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