ArticleComputational and structural biotechnology journal2019
Computational identification of vesicular transport proteins from sequences using deep gated recurrent units architecture.
Article in Computational and structural biotechnology journal, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 35 papers.
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35 citing papers in PubMed.
- Comparative review of artificial intelligence for transcriptomic biomarker discovery in coronavirus disease 2019 (COVID-19).Briefings in bioinformatics · 2026Review
- BiGvCL: bipartite graph-based cross-domain contrastive learning model for the predicting drug-gene interactions.Briefings in bioinformatics · 2026Article
- An Explainable Deep Learning Model for Clathrin Protein Prediction Using a DCT-Enhanced Position-Specific Scoring Matrix.Current drug targets · 2026Article
- Accurate Asthma-COPD Overlap Classification via Deep Transfer Learning in Medical Image Segmentation.International journal of chronic obstructive pulmonary disease · 2026Article
- Integrating ESM‑2 and Graph Neural Networks with AlphaFold‑2 Structures for Enhanced Protein Function Prediction.ACS omega · 2025Article
- Exploring Mechanisms and Biomarkers of Breast Cancer Invasion and Migration: An Explainable Gene-Pathway-Compounds Neural Network.Cancer medicine · 2025Article
- GA-TongueNet: tongue image segmentation network using innovative DiFP and MDi for stable generalization ability.Frontiers in physiology · 2025Article
- TMEM35B as a novel biomarker for diagnosing gliomas.Biomarkers in medicine · 2024Article
- Synergism of vesicle trafficking and cytoskeleton during regulation of plant growth and development: A mechanistic outlook.Heliyon · 2023Review
- Complement System Inhibitory Drugs in a Zebrafish (International journal of molecular sciences · 2023Article
- Stack-VTP: prediction of vesicle transport proteins based on stacked ensemble classifier and evolutionary information.BMC bioinformatics · 2023Article
- Using biological information to analyze potential miRNA-mRNA regulatory networks in the plasma of patients with non-small cell lung cancer.BMC cancer · 2022Article
- Identification of Vesicle Transport ProteinsFrontiers in genetics · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Autophagy-related prognostic signature for survival prediction of triple negative breast cancer.PeerJ · 2022Article
- Using BioPAX-Parser (BiP) to enrich lists of genes or proteins with pathway data.BMC bioinformatics · 2021Article
- VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.Frontiers in genetics · 2021Article
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5 authors.
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Abstract
Protein function prediction is one of the most well-studied topics, attracting attention from countless researchers in the field of computational biology. Implementing deep neural networks that help improve the prediction of protein function, however, is still a major challenge. In this research, we suggested a new strategy that includes gated recurrent units and position-specific scoring matrix profiles to predict vesicular transportation proteins, a biological function of great importance. Although it is difficult to discover its function, our model is able to achieve accuracies of 82.3% and 85.8% in the cross-validation and independent dataset, respectively. We also solve the problem of imbalance in the dataset via tuning class weight in the deep learning model. The results generated showed sensitivity, specificity, MCC, and AUC to have values of 79.2%, 82.9%, 0.52, and 0.861, respectively. Our strategy shows superiority in results on the same dataset against all other state-of-the-art algorithms. In our suggested research, we have suggested a technique for the discovery of more proteins, particularly proteins connected with vesicular transport. In addition, our accomplishment could encourage the use of gated recurrent units architecture in protein function prediction.
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