A Deep Learning and PSSM Profile Approach for Accurate SNARE Protein Prediction.

Quang Hien Kha, Huu Phuc Lam Nguyen, Nguyen Quoc Khanh Le

Abstract read

In one paragraph

Article in Methods in molecular biology (Clifton, N.J.), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An efficient framework for protein-protein interaction prediction by integrating stacked denoising autoencoders and random ferns.iScience · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Quang Hien KhaAIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.

Huu Phuc Lam NguyenAIBioMed Research Group, Taipei Medical University, Taipei, Taiwan.

Nguyen Quoc Khanh LeAIBioMed Research Group, Taipei Medical University, Taipei, Taiwan. khanhlee@tmu.edu.tw.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

SNARE proteins play a pivotal role in membrane fusion and various cellular processes. Accurate identification of SNARE proteins is crucial for elucidating their functions in both health and disease contexts. This chapter presents a novel approach employing multiscan convolutional neural networks (CNNs) combined with position-specific scoring matrix (PSSM) profiles to accurately recognize SNARE proteins. By leveraging deep learning techniques, our method significantly enhances the accuracy and efficacy of SNARE protein classification. We detail the step-by-step methodology, including dataset preparation, feature extraction using PSI-BLAST, and the design of the multiscan CNN architecture. Our results demonstrate that this approach outperforms existing methods, providing a robust and reliable tool for bioinformatics research.

Indexed as

Computational BiologyDeep LearningPosition-Specific Scoring MatricesSNARE ProteinsDatabases, ProteinHumansNeural Networks, ComputerSNARE ProteinsBioinformaticsConvolutional neural networks (CNN)Deep learningPosition-specific scoring matrix (PSSM)Protein classificationSNARE proteins

Identifiers

PMID39806147

DOI10.1093/bib/bbab352

What OpenQuestion holds

Textmetadata

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Registered trials

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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.