ArticleBMC bioinformatics2019
Accurate classification of membrane protein types based on sequence and evolutionary information using deep learning.
Article in BMC bioinformatics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- Bioengineering of extracellular vesicles with scaffold proteins for drug delivery.Journal of nanobiotechnology · 2026Review
- RSLpred2: An Integrated Web Server for the Annotation of Rice Proteome Subcellular Localization Using Deep Learning.Rice (New York, N.Y.) · 2025Article
- Engineered Proteins and Chemical Tools to Probe the Cell Surface Proteome.Chemical reviews · 2025Review
- Protein engineering in the deep learning era.mLife · 2024Review
- Hybrid framework for membrane protein type prediction based on the PSSM.Scientific reports · 2024Article
- CAT-CBAM-Net: An Automatic Scoring Method for Sow Body Condition Based on CNN and Transformer.Sensors (Basel, Switzerland) · 2023Article
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- Article
- Machine learning in computational modelling of membrane protein sequences and structures: From methodologies to applications.Computational and structural biotechnology journal · 2023Review
- Review
- Review
- A deep learning approach for orphan gene identification in moso bamboo (Phyllostachys edulis) based on the CNN + Transformer model.BMC bioinformatics · 2022Article
- Domain Expansion and Functional Diversification in Vertebrate Reproductive Proteins.Molecular biology and evolution · 2022Article
- Predicting protein-membrane interfaces of peripheral membrane proteins using ensemble machine learning.Briefings in bioinformatics · 2022Article
- Segmenting the Semi-Conductive Shielding Layer of Cable Slice Images Using the Convolutional Neural Network.Polymers · 2020Article
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Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundMembrane proteins play an important role in the life activities of organisms. Knowing membrane protein types provides clues for understanding the structure and function of proteins. Though various computational methods for predicting membrane protein types have been developed, the results still do not meet the expectations of researchers.
resultsWe propose two deep learning models to process sequence information and evolutionary information, respectively. Both models obtained better results than traditional machine learning models. Furthermore, to improve the performance of the sequence information model, we also provide a new vector representation method to replace the one-hot encoding, whose overall success rate improved by 3.81% and 6.55% on two datasets. Finally, a more effective model is obtained by fusing the above two models, whose overall success rate reached 95.68% and 92.98% on two datasets.
conclusionThe final experimental results show that our method is more effective than existing methods for predicting membrane protein types, which can help laboratory researchers to identify the type of novel membrane proteins.
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