ArticleBioinformatics (Oxford, England)2021
Identification of sub-Golgi protein localization by use of deep representation learning features.
Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed.
- Article
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- Identification of plant vacuole proteins by using graph neural network and contact maps.BMC bioinformatics · 2023Article
- Protein Fitness Prediction Is Impacted by the Interplay of Language Models, Ensemble Learning, and Sampling Methods.Pharmaceutics · 2023Article
- Prediction of protein solubility based on sequence physicochemical patterns and distributed representation information with DeepSoluE.BMC biology · 2023Article
- Prediction of apoptosis protein subcellular location based on amphiphilic pseudo amino acid composition.Frontiers in genetics · 2023Article
- Identify Bitter Peptides by Using Deep Representation Learning Features.International journal of molecular sciences · 2022Article
- A polygenic stacking classifier revealed the complicated platelet transcriptomic landscape of adult immune thrombocytopenia.Molecular therapy. Nucleic acids · 2022Article
- SYNBIP: synthetic binding proteins for research, diagnosis and therapy.Nucleic acids research · 2022Article
- Bibliometric analysis of artificial intelligence for biotechnology and applied microbiology: Exploring research hotspots and frontiers.Frontiers in bioengineering and biotechnology · 2022Review
- iThermo: A Sequence-Based Model for Identifying Thermophilic Proteins Using a Multi-Feature Fusion Strategy.Frontiers in microbiology · 2022Article
- MoRF-FUNCpred: Molecular Recognition Feature Function Prediction Based on Multi-Label Learning and Ensemble Learning.Frontiers in pharmacology · 2022Review
- PredMHC: An Effective Predictor of Major Histocompatibility Complex Using Mixed Features.Frontiers in genetics · 2022Article
- Editorial: Methods and Applications in Molecular Phylogenetics.Frontiers in genetics · 2022Article
- Recent Advances in the Prediction of Subcellular Localization of Proteins and Related Topics.Frontiers in bioinformatics · 2022Review
- Biological Sequence Classification: A Review on Data and General Methods.Research (Washington, D.C.) · 2022Article
- Identification of plant vacuole proteins by exploiting deep representation learning features.Computational and structural biotechnology journal · 2022Article
- Amino Acid Reduction Can Help to Improve the Identification of Antimicrobial Peptides and Their Functional Activities.Frontiers in genetics · 2021Article
- Prediction of Metal Ion Binding Sites of Transmembrane Proteins.Computational and mathematical methods in medicine · 2021Article
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Authors and funding
4 authors.
Funding
Abstract
motivationThe Golgi apparatus has a key functional role in protein biosynthesis within the eukaryotic cell with malfunction resulting in various neurodegenerative diseases. For a better understanding of the Golgi apparatus, it is essential to identification of sub-Golgi protein localization. Although some machine learning methods have been used to identify sub-Golgi localization proteins by sequence representation fusion, more accurate sub-Golgi protein identification is still challenging by existing methodology.
resultswe developed a protein sub-Golgi localization identification protocol using deep representation learning features with 107 dimensions. By this protocol, we demonstrated that instead of multi-type protein sequence feature representation fusion as in previous state-of-the-art sub-Golgi-protein localization classifiers, it is sufficient to exploit only one type of feature representation for more accurately identification of sub-Golgi proteins. Compared with independent testing results for benchmark datasets, our protocol is able to perform generally, reliably and robustly for sub-Golgi protein localization prediction. AVAILABILITYAND IMPLEMENTATION: A use-friendly webserver is freely accessible at http://isGP-DRLF.aibiochem.net and the prediction code is accessible at https://github.com/zhibinlv/isGP-DRLF. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.
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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.