ArticleFrontiers in physiology2019
Identifying SNAREs by Incorporating Deep Learning Architecture and Amino Acid Embedding Representation.
Article in Frontiers in physiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers, 1 of them a synthesis that pooled it.
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Who cites it
28 citing papers in PubMed, 1 synthesis or guideline pooled it, 60 citations in OpenAlex.
- Protein Sequence Analysis landscape: A Systematic Review of Task Types, Databases, Datasets, Word Embeddings Methods, and Language Models.Database : the journal of biological databases and curation · 2025Pooled it
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- The Classification of Lumbar Spondylolisthesis X-Ray Images Using Convolutional Neural Networks.Journal of imaging informatics in medicine · 2024Article
- PPSNO: A Feature-Rich SNO Sites Predictor by Stacking Ensemble Strategy from Protein Sequence-Derived Information.Interdisciplinary sciences, computational life sciences · 2024Article
- Deep learning system assisted detection and localization of lumbar spondylolisthesis.Frontiers in bioengineering and biotechnology · 2023Article
- Exploiting multi-granularity visual features for retinal layer segmentation in human eyes.Frontiers in bioengineering and biotechnology · 2023Article
- Organizing the bacterial annotation space with amino acid sequence embeddings.BMC bioinformatics · 2022Article
- Multi-Information Model for Large-Flowered Chrysanthemum Cultivar Recognition and Classification.Frontiers in plant science · 2022Article
- Predicting novel drug candidates against Covid-19 using generative deep neural networks.Journal of molecular graphics & modelling · 2022Article
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- Prediction of Peptide Detectability Based on CapsNet and Convolutional Block Attention Module.International journal of molecular sciences · 2021Article
- Application of Artificial Neural Network Based on Traditional Detection and GC-MS in Prediction of Free Radicals in Thermal Oxidation of Vegetable Oil.Molecules (Basel, Switzerland) · 2021Article
- nhKcr: a new bioinformatics tool for predicting crotonylation sites on human nonhistone proteins based on deep learning.Briefings in bioinformatics · 2021Article
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- Deep-Learning-Based Approach to Anomaly Detection Techniques for Large Acoustic Data in Machine Operation.Sensors (Basel, Switzerland) · 2021Article
- In-Pero: Exploiting Deep Learning Embeddings of Protein Sequences to Predict the Localisation of Peroxisomal Proteins.International journal of molecular sciences · 2021Article
- New Sensor Data Structuring for Deeper Feature Extraction in Human Activity Recognition.Sensors (Basel, Switzerland) · 2021Article
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Corrections and comments
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Authors and funding
2 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
SNAREs (soluble N-ethylmaleimide-sensitive factor activating protein receptors) are a group of proteins that are crucial for membrane fusion and exocytosis of neurotransmitters from the cell. They play an important role in a broad range of cell processes, including cell growth, cytokinesis, and synaptic transmission, to promote cell membrane integration in eukaryotes. Many studies determined that SNARE proteins have been associated with a lot of human diseases, especially in cancer. Therefore, identifying their functions is a challenging problem for scientists to better understand the cancer disease as well as design the drug targets for treatment. We described each protein sequence based on the amino acid embeddings using fastText, which is a natural language processing model performing well in its field. Because each protein sequence is similar to a sentence with different words, applying language model into protein sequence is challenging and promising. After generating, the amino acid embedding features were fed into a deep learning algorithm for prediction. Our model which combines fastText model and deep convolutional neural networks could identify SNARE proteins with an independent test accuracy of 92.8%, sensitivity of 88.5%, specificity of 97%, and Matthews correlation coefficient (MCC) of 0.86. Our performance results were superior to the state-of-the-art predictor (SNARE-CNN). We suggest this study as a reliable method for biologists for SNARE identification and it serves a basis for applying fastText word embedding model into bioinformatics, especially in protein sequencing prediction.
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