ArticleInternational journal of molecular sciences2014
PseAAC-General: fast building various modes of general form of Chou's pseudo-amino acid composition for large-scale protein datasets.
Article in International journal of molecular sciences, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 73 papers, 1 of them a synthesis that pooled it.
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Who cites it
73 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Current computational tools for protein lysine acylation site prediction.Briefings in bioinformatics · 2024Pooled it
- ProtDML: label-aware representation learning for broad-spectrum protein function prediction.Briefings in bioinformatics · 2026Article
- Enhancing the Feature Representation of Protein Sequence Descriptors in Protein-Protein Interaction Prediction.Interdisciplinary sciences, computational life sciences · 2025Article
- Predicting viral proteins that evade the innate immune system: a machine learning-based immunoinformatics tool.BMC bioinformatics · 2024Article
- Review and perspective on bioinformatics tools using machine learning and deep learning for predicting antiviral peptides.Molecular diversity · 2024Review
- Diff-AMP: tailored designed antimicrobial peptide framework with all-in-one generation, identification, prediction and optimization.Briefings in bioinformatics · 2024Article
- Mining Bovine Milk Proteins for DPP-4 Inhibitory Peptides Using Machine Learning and Virtual Proteolysis.Research (Washington, D.C.) · 2024Article
- MDTips: a multimodal-data-based drug-target interaction prediction system fusing knowledge, gene expression profile, and structural data.Bioinformatics (Oxford, England) · 2023Article
- A machine learning technique for identifying DNA enhancer regions utilizing CIS-regulatory element patterns.Scientific reports · 2022Article
- iFeatureOmega: an integrative platform for engineering, visualization and analysis of features from molecular sequences, structural and ligand data sets.Nucleic acids research · 2022Article
- Multi-feature Fusion Method Based on Linear Neighborhood Propagation Predict Plant LncRNA-Protein Interactions.Interdisciplinary sciences, computational life sciences · 2022Article
- To Assist Oncologists: An Efficient Machine Learning-Based Approach for Anti-Cancer Peptides Classification.Sensors (Basel, Switzerland) · 2022Article
- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 2022Article
- Identify RNA-associated subcellular localizations based on multi-label learning using Chou's 5-steps rule.BMC genomics · 2021Article
- iSUMOK-PseAAC: prediction of lysine sumoylation sites using statistical moments and Chou's PseAAC.PeerJ · 2021Article
- Use Chou's 5-steps rule to identify DNase I hypersensitive sites via dinucleotide property matrix and extreme gradient boosting.Molecular genetics and genomics : MGG · 2020Article
- Use of Chou's 5-steps rule to predict the subcellular localization of gram-negative and gram-positive bacterial proteins by multi-label learning based on gene ontology annotation and profile alignment.Journal of integrative bioinformatics · 2020Article
- Identification of Human Secretome and Membrane Proteome-Based Cancer Biomarkers Utilizing Bioinformatics.The Journal of membrane biology · 2020Article
- Article
- Characterization of the relationship between FLI1 and immune infiltrate level in tumour immune microenvironment for breast cancer.Journal of cellular and molecular medicine · 2020Article
13 more citing papers are in PubMed but not listed here.
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The general form pseudo-amino acid composition (PseAAC) has been widely used to represent protein sequences in predicting protein structural and functional attributes. We developed the program PseAAC-General to generate various different modes of Chou's general PseAAC, such as the gene ontology mode, the functional domain mode, and the sequential evolution mode. This program allows the users to define their own desired modes. In every mode, 544 physicochemical properties of the amino acids are available for choosing. The computing efficiency is at least 100 times that of existing programs, which makes it able to facilitate the extensive studies on proteins and peptides. The PseAAC-General is freely available via SourceForge. It runs on both Linux and Windows.
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