ArticleProteome science2009
Identification of protein functions using a machine-learning approach based on sequence-derived properties.
Article in Proteome science, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed.
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- The Novel Role of Tyrosinase Enzymes in the Storage of Globally Significant Amounts of Carbon in Wetland Ecosystems.Environmental science & technology · 2022Review
- Mitotic chromosome binding predicts transcription factor properties in interphase.Nature communications · 2019Article
- Consistent prediction of GO protein localization.Scientific reports · 2018Article
- Article
- GRAFENE: Graphlet-based alignment-free network approach integrates 3D structural and sequence (residue order) data to improve protein structural comparison.Scientific reports · 2017Article
- A Meta-Analysis Based Method for Prioritizing Candidate Genes Involved in a Pre-specific Function.Frontiers in plant science · 2016Article
- A Factor Graph Approach to Automated GO Annotation.PloS one · 2016Article
- A survey of computational intelligence techniques in protein function prediction.International journal of proteomics · 2014Review
- Prediction of detailed enzyme functions and identification of specificity determining residues by random forests.PloS one · 2014Article
- Identification of MFS proteins in sorghum using semantic similarity.Theory in biosciences = Theorie in den Biowissenschaften · 2013Article
- An improved sequence based prediction protocol for DNA-binding proteins using SVM and comprehensive feature analysis.BMC bioinformatics · 2013Article
- A novel method for classifying body mass index on the basis of speech signals for future clinical applications: a pilot study.Evidence-based complementary and alternative medicine : eCAM · 2013Article
- Identification of salt treated proteins in sorghum using gene ontology linkage.Physiology and molecular biology of plants : an international journal of functional plant biology · 2012Article
- A new method of diagnosing constitutional types based on vocal and facial features for personalized medicine.Journal of biomedicine & biotechnology · 2012Article
- clusterMaker: a multi-algorithm clustering plugin for Cytoscape.BMC bioinformatics · 2011Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundPredicting the function of an unknown protein is an essential goal in bioinformatics. Sequence similarity-based approaches are widely used for function prediction; however, they are often inadequate in the absence of similar sequences or when the sequence similarity among known protein sequences is statistically weak. This study aimed to develop an accurate prediction method for identifying protein function, irrespective of sequence and structural similarities.
resultsA highly accurate prediction method capable of identifying protein function, based solely on protein sequence properties, is described. This method analyses and identifies specific features of the protein sequence that are highly correlated with certain protein functions and determines the combination of protein sequence features that best characterises protein function. Thirty-three features that represent subtle differences in local regions and full regions of the protein sequences were introduced. On the basis of 484 features extracted solely from the protein sequence, models were built to predict the functions of 11 different proteins from a broad range of cellular components, molecular functions, and biological processes. The accuracy of protein function prediction using random forests with feature selection ranged from 94.23% to 100%. The local sequence information was found to have a broad range of applicability in predicting protein function.
conclusionWe present an accurate prediction method using a machine-learning approach based solely on protein sequence properties. The primary contribution of this paper is to propose new PNPRD features representing global and/or local differences in sequences, based on positively and/or negatively charged residues, to assist in predicting protein function. In addition, we identified a compact and useful feature subset for predicting the function of various proteins. Our results indicate that sequence-based classifiers can provide good results among a broad range of proteins, that the proposed features are useful in predicting several functions, and that the combination of our and traditional features may support the creation of a discriminative feature set for specific protein functions.
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Registered trials
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.