ArticlePeerJ. Computer science2019
SNARE-CNN: a 2D convolutional neural network architecture to identify SNARE proteins from high-throughput sequencing data.
Article in PeerJ. Computer science, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 30 papers.
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Who cites it
30 citing papers in PubMed.
- Upcycling Vegetable Waste Into Functional Food Ingredients via Synergistic Microbial Engineering and Artificial Intelligence.Comprehensive reviews in food science and food safety · 2026Review
- A Deep Learning and PSSM Profile Approach for Accurate SNARE Protein Prediction.Methods in molecular biology (Clifton, N.J.) · 2025Article
- Choosing an appropriate somatic embryogenesis medium of carrot (Daucus carota L.) by data mining technology.BMC biotechnology · 2024Article
- Early detection of abiotic stress in plants through SNARE proteins using hybrid feature fusion model.PeerJ. Computer science · 2024Article
- Identifying SNARE Proteins Using an Alignment-Free Method Based on Multiscan Convolutional Neural Network and PSSM Profiles.Journal of chemical information and modeling · 2022Article
- SNARER: new molecular descriptors for SNARE proteins classification.BMC bioinformatics · 2022Article
- Identification of the ubiquitin-proteasome pathway domain by hyperparameter optimization based on a 2D convolutional neural network.Frontiers in genetics · 2022Article
- A GHKNN model based on the physicochemical property extraction method to identify SNARE proteins.Frontiers in genetics · 2022Article
- Deep Learning Analysis of CT Images Reveals High-Grade Pathological Features to Predict Survival in Lung Adenocarcinoma.Cancers · 2021Article
- Improving protein domain classification for third-generation sequencing reads using deep learning.BMC genomics · 2021Article
- VTP-Identifier: Vesicular Transport Proteins Identification Based on PSSM Profiles and XGBoost.Frontiers in genetics · 2021Article
- A SNARE Protein Identification Method Based on iLearnPlus to Efficiently Solve the Data Imbalance Problem.Frontiers in genetics · 2021Article
- Genetic variations analysis for complex brain disease diagnosis using machine learning techniques: opportunities and hurdles.PeerJ. Computer science · 2021Article
- A deep learning algorithm to detect coronavirus (COVID-19) disease using CT images.PeerJ. Computer science · 2021Article
- Introspective analysis of convolutional neural networks for improving discrimination performance and feature visualisation.PeerJ. Computer science · 2021Article
- Full depth CNN classifier for handwritten and license plate characters recognition.PeerJ. Computer science · 2021Article
- Diagnosis of vertebral column pathologies using concatenated resampling with machine learning algorithms.PeerJ. Computer science · 2021Article
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- Gutter oil detection for food safety based on multi-feature machine learning and implementation on FPGA with approximate multipliers.PeerJ. Computer science · 2021Article
- Deep Learning Algorithms Achieved Satisfactory Predictions When Trained on a Novel Collection of Anticoronavirus Molecules.Frontiers in genetics · 2021Article
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2 authors.
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Abstract
Deep learning has been increasingly and widely used to solve numerous problems in various fields with state-of-the-art performance. It can also be applied in bioinformatics to reduce the requirement for feature extraction and reach high performance. This study attempts to use deep learning to predict SNARE proteins, which is one of the most vital molecular functions in life science. A functional loss of SNARE proteins has been implicated in a variety of human diseases (e.g., neurodegenerative, mental illness, cancer, and so on). Therefore, creating a precise model to identify their functions is a crucial problem for understanding these diseases, and designing the drug targets. Our SNARE-CNN model which uses two-dimensional convolutional neural networks and position-specific scoring matrix profiles could identify SNARE proteins with achieved sensitivity of 76.6%, specificity of 93.5%, accuracy of 89.7%, and MCC of 0.7 in cross-validation dataset. We also evaluate the performance of our model via an independent dataset and the result shows that we are able to solve the overfitting problem. Compared with other state-of-the-art methods, this approach achieved significant improvement in all of the metrics. Throughout the proposed study, we provide an effective model for identifying SNARE proteins and a basis for further research that can apply deep learning in bioinformatics, especially in protein function prediction. SNARE-CNN are freely available at https://github.com/khanhlee/snare-cnn.
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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 Socratic graph.