ArticleBMC bioinformatics2021
FEGS: a novel feature extraction model for protein sequences and its applications.
Article in BMC bioinformatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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17 citing papers in PubMed.
- NToxSEM: Enhancing prediction of neurotoxic peptides and neurotoxins using a stacked ensemble-based multimodal framework.Protein science : a publication of the Protein Society · 2026Article
- ProtDML: label-aware representation learning for broad-spectrum protein function prediction.Briefings in bioinformatics · 2026Article
- Computational models for the classification of antibody specificity using heavy chain features.PloS one · 2026Article
- Accurate identification of broadly neutralizing antibodies against dengue virus based on deep stacking strategy with multi-perspective features.Scientific reports · 2025Article
- GRU4ACE: Enhancing ACE inhibitory peptide prediction by integrating gated recurrent unit with multi-source feature embeddings.Protein science : a publication of the Protein Society · 2025Article
- Overview and Prospects of DNA Sequence Visualization.International journal of molecular sciences · 2025Review
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- ToxGIN: an In silico prediction model for peptide toxicity via graph isomorphism networks integrating peptide sequence and structure information.Briefings in bioinformatics · 2024Article
- A Computational Predictor for Accurate Identification of Tumor Homing Peptides by Integrating Sequential and Deep BiLSTM Features.Interdisciplinary sciences, computational life sciences · 2024Article
- An Augmented Sample Selection Framework for Prediction of Anticancer Peptides.Molecules (Basel, Switzerland) · 2023Article
- Design of a Synthetic Long Peptide Vaccine Targeting HPV-16 and -18 Using Immunoinformatic Methods.Pharmaceutics · 2023Article
- Accelerating the Discovery of Anticancer Peptides through Deep Forest Architecture with Deep Graphical Representation.International journal of molecular sciences · 2023Article
- IGPred-HDnet: Prediction of Immunoglobulin Proteins Using Graphical Features and the Hierarchal Deep Learning-Based Approach.Computational intelligence and neuroscience · 2023Article
- Improving antibody optimization ability of generative adversarial network through large language model.Computational and structural biotechnology journal · 2023Article
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- FFP: joint Fast Fourier transform and fractal dimension in amino acid property-aware phylogenetic analysis.BMC bioinformatics · 2022Article
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6 authors.
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Abstract
backgroundFeature extraction of protein sequences is widely used in various research areas related to protein analysis, such as protein similarity analysis and prediction of protein functions or interactions.
resultsIn this study, we introduce FEGS (Feature Extraction based on Graphical and Statistical features), a novel feature extraction model of protein sequences, by developing a new technique for graphical representation of protein sequences based on the physicochemical properties of amino acids and effectively employing the statistical features of protein sequences. By fusing the graphical and statistical features, FEGS transforms a protein sequence into a 578-dimensional numerical vector. When FEGS is applied to phylogenetic analysis on five protein sequence data sets, its performance is notably better than all of the other compared methods.
conclusionThe FEGS method is carefully designed, which is practically powerful for extracting features of protein sequences. The current version of FEGS is developed to be user-friendly and is expected to play a crucial role in the related studies of protein sequence analyses.
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