ArticleBioinformatics (Oxford, England)2023
ExamPle: explainable deep learning framework for the prediction of plant small secreted peptides.
Article in Bioinformatics (Oxford, England), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- 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
- PFGPred: a stack ensemble classifier for the identification of fusion genes in plants.DNA research : an international journal for rapid publication of reports on genes and genomes · 2026Article
- NeuroPpred-MSN: A Neuropeptide Prediction Model Based on Multi-feature Fusion and Siamese Networks.Interdisciplinary sciences, computational life sciences · 2026Article
- AIP-TranLAC: A Transformer-Based Method Integrating LSTM and Attention Mechanism for Predicting Anti-inflammatory Peptides.Interdisciplinary sciences, computational life sciences · 2026Article
- Peptide Hormone-Mediated Regulation of Plant Development and Environmental Adaptability.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Review
- iACP-DPNet: a dual-pooling causal dilated convolutional network for interpretable anticancer peptide identification.Functional & integrative genomics · 2025Article
- A two-task predictor for discovering phase separation proteins and their undergoing mechanism.Briefings in bioinformatics · 2024Article
- MultiFeatVotPIP: a voting-based ensemble learning framework for predicting proinflammatory peptides.Briefings in bioinformatics · 2024Article
- A deep learning model for anti-inflammatory peptides identification based on deep variational autoencoder and contrastive learning.Scientific reports · 2024Article
- ACP-CapsPred: an explainable computational framework for identification and functional prediction of anticancer peptides based on capsule network.Briefings in bioinformatics · 2024Article
- Research Progress of Small Plant Peptides on the Regulation of Plant Growth, Development, and Abiotic Stress.International journal of molecular sciences · 2024Review
- Improved super-resolution ribosome profiling reveals prevalent translation of upstream ORFs and small ORFs in Arabidopsis.The Plant cell · 2024Article
- Prediction of SARS-CoV-2 Infection Phosphorylation Sites and Associations of these Modifications with Lung Cancer Development.Current gene therapy · 2024Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
motivationPlant Small Secreted Peptides (SSPs) play an important role in plant growth, development, and plant-microbe interactions. Therefore, the identification of SSPs is essential for revealing the functional mechanisms. Over the last few decades, machine learning-based methods have been developed, accelerating the discovery of SSPs to some extent. However, existing methods highly depend on handcrafted feature engineering, which easily ignores the latent feature representations and impacts the predictive performance.
resultsHere, we propose ExamPle, a novel deep learning model using Siamese network and multi-view representation for the explainable prediction of the plant SSPs. Benchmarking comparison results show that our ExamPle performs significantly better than existing methods in the prediction of plant SSPs. Also, our model shows excellent feature extraction ability. Importantly, by utilizing in silicomutagenesis experiment, ExamPle can discover sequential characteristics and identify the contribution of each amino acid for the predictions. The key novel principle learned by our model is that the head region of the peptide and some specific sequential patterns are strongly associated with the SSPs' functions. Thus, ExamPle is expected to be a useful tool for predicting plant SSPs and designing effective plant SSPs. AVAILABILITY AND IMPLEMENTATION: Our codes and datasets are available at https://github.com/Johnsunnn/ExamPle.
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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.