ArticleBMC bioinformatics2007
Efficacy of different protein descriptors in predicting protein functional families.
Article in BMC bioinformatics, 2007. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 1 of them a synthesis that pooled it.
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Who cites it
32 citing papers in PubMed, 1 synthesis or guideline pooled it.
- COVID-19 Coronavirus Vaccine Design Using Reverse Vaccinology and Machine Learning.Frontiers in immunology · 2020Pooled it
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- Tumor microenvironment-targeted PROTAC nanoparticle self-assembly broadly predicted by structural descriptors.Science advances · 2025Article
- AAindexNC: Estimating the Physicochemical Properties of Non-Canonical Amino Acids, Including Those Derived from the PDB and PDBeChem Databank.International journal of molecular sciences · 2024Article
- Protein characteristics substantially influence the propensity of activity cliffs among kinase inhibitors.Scientific reports · 2024Article
- Physical-Chemical Features Selection Reveals That Differences in Dipeptide Compositions Correlate Most with Protein-Protein Interactions.bioRxiv : the preprint server for biology · 2024Article
- How to approach machine learning-based prediction of drug/compound-target interactions.Journal of cheminformatics · 2023Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
- Virtual screening of Indonesian herbal compounds as COVID-19 supportive therapy: machine learning and pharmacophore modeling approaches.BMC complementary medicine and therapies · 2022Article
- SNARER: new molecular descriptors for SNARE proteins classification.BMC bioinformatics · 2022Article
- FEPS: A Tool for Feature Extraction from Protein Sequence.Methods in molecular biology (Clifton, N.J.) · 2022Article
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- Density of Deep Eutectic Solvents: The Path Forward Cheminformatics-Driven Reliable Predictions for Mixtures.Molecules (Basel, Switzerland) · 2021Article
- Machine Learning Uncovers Adverse Drug Effects on Intestinal Bacteria.Pharmaceutics · 2021Article
- Drug Target Identification with Machine Learning: How to Choose Negative Examples.International journal of molecular sciences · 2021Article
- Vaxign-ML: supervised machine learning reverse vaccinology model for improved prediction of bacterial protective antigens.Bioinformatics (Oxford, England) · 2020Article
- COVID-19 coronavirus vaccine design using reverse vaccinology and machine learning.bioRxiv : the preprint server for biology · 2020Article
- Evaluation of deep and shallow learning methods in chemogenomics for the prediction of drugs specificity.Journal of cheminformatics · 2020Article
- Recent applications of deep learning and machine intelligence on in silico drug discovery: methods, tools and databases.Briefings in bioinformatics · 2019Review
- PrESOgenesis: A two-layer multi-label predictor for identifying fertility-related proteins using support vector machine and pseudo amino acid composition approach.Scientific reports · 2018Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSequence-derived structural and physicochemical descriptors have frequently been used in machine learning prediction of protein functional families, thus there is a need to comparatively evaluate the effectiveness of these descriptor-sets by using the same method and parameter optimization algorithm, and to examine whether the combined use of these descriptor-sets help to improve predictive performance. Six individual descriptor-sets and four combination-sets were evaluated in support vector machines (SVM) prediction of six protein functional families.
resultsThe performance of these descriptor-sets were ranked by Matthews correlation coefficient (MCC), and categorized into two groups based on their performance. While there is no overwhelmingly favourable choice of descriptor-sets, certain trends were found. The combination-sets tend to give slightly but consistently higher MCC values and thus overall best performance such that three out of four combination-sets show slightly better performance compared to one out of six individual descriptor-sets.
conclusionOur study suggests that currently used descriptor-sets are generally useful for classifying proteins and the prediction performance may be enhanced by exploring combinations of descriptors.
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