Fast Fourier Transform-based Support Vector Machine for Prediction of G-protein Coupled Receptor Subfamilies

Guo, Yanzhi; Li, Menglong; Wang, Ke-Long; Wen, Zhining; Lu, M.; Liu, Lixia; Jiang, Lin

doi:10.1111/j.1745-7270.2005.00110.x

Cited by 16 publications

(5 citation statements)

References 36 publications

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“…Several publicly available SVM-based GPCR classifiers exist. PRED-GPCR (http://athina.biol.uoa.gr/bioinformatics/PRED-GPCR/) (Papasaikas et al, 2004;Guo et al, 2005) was developed as a fast fourier transform with SVMs on the basis of the hydrophobicity of the amino acid sequence. Quantitative descriptions of the proteins relating to hydrophobicity, bulk and electronic properties were derived from the hydrophobicity model, composition-polarity-volume (c-p-v) model and the electron-ion interaction potential (EIIP) model.…”

Section: Gpcr Prediction Serversmentioning

confidence: 99%

On the hierarchical classification of G protein-coupled receptors

et al. 2007

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An alignment-free approach to GPCR classification has been developed using techniques drawn from data mining and proteochemometrics. A dataset of over 8000 sequences was constructed to train the algorithm. This represents one of the largest GPCR datasets currently available. A predictive algorithm was developed based upon the simplest reasonable numerical representation of the protein's physicochemical properties. A selective top-down approach was developed, which used a hierarchical classifier to assign sequences to subdivisions within the GPCR hierarchy. The predictive performance of the algorithm was assessed against several standard data mining classifiers and further validated against Support Vector Machine-based GPCR prediction servers. The selective top-down approach achieves significantly higher accuracy than standard data mining methods in almost all cases.

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Section: Gpcr Prediction Serversmentioning

confidence: 99%

On the hierarchical classification of G protein-coupled receptors

et al. 2007

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“…Although SVMs are more commonly used to solve 2-class problems, this technique can be applied to the classification of GPCR data by successively trying to classify one class against all others. Several publicly available SVM-based GPCR classifiers exist including PRED-GPCR (Papasaikas et al, 2004, Guo et al, 2005, GPCRPred (Bhasin et al, 2004) and, GPCRsclass (Bhasin et al, 2005), which concentrate on the Class A aminergic receptor subfamily. In the first round of analysis, an SVM is generated to distinguish amines from all other GPCRs.…”

Section: Application Of Alignment-free Tech-niquesmentioning

confidence: 99%

Alignment-Independent Techniques for Protein Classification

Davies¹,

Secker²,

Freitas³

et al. 2008

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Predicting protein structure and function from amino acid sequences is a central aim of bioinformatics. Most bioinformatics analyses use sequence alignment as the basis by which to measure similarity. However, there is increasing evidence that many protein families are resistant to this straightforward method of comparison. Increasingly, a combination of machine-learning techniques and abstract representations of protein sequences is being used to classify proteins based upon the similarity of their physico-chemical properties rather than scoring sequence alignments. This is particularly effective in protein families that show greater structural conservation but appear to lack conserved sequences. Here we describe the inherent limitations of the alignment-dependent approaches to protein classification and present 'alignment-free' representations as a viable and realistic alternative to solve complex problems within bioinformatics.

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“…We selected three common physicochemical properties, hydrophobicity [49], volumes of side chains of amino acids [50], and polarity [51], to represent the structure and function [52], the stereospecific blockade [53] and the electronic property [54] of residues in a protein respectively. These original values were taken from Guo et al [55] and were first normalized to zero mean value and unit standard deviation (SD) by Equation 1:…”

Section: Auto Covariance (Ac)mentioning

confidence: 99%

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

Xiao¹,

Guo²,

Zeng³

et al. 2009

JBiSE

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The knowledge of subnuclear localization in eukaryotic cells is indispensable for under-standing the biological function of nucleus, genome regulation and drug discovery. In this study, a new feature representation was pro-posed by combining position specific scoring matrix (PSSM) and auto covariance (AC). The AC variables describe the neighboring effect between two amino acids, so that they incorpo-rate the sequence-order information; PSSM de-scribes the information of biological evolution of proteins. Based on this new descriptor, a support vector machine (SVM) classifier was built to predict subnuclear localization. To evaluate the power of our predictor, the benchmark dataset that contains 714 proteins localized in nine subnuclear compartments was utilized. The total jackknife cross validation ac-curacy of our method is 76.5%, that is higher than those of the Nuc-PLoc (67.4%), the OET- KNN (55.6%), AAC based SVM (48.9%) and ProtLoc (36.6%). The prediction software used in this article and the details of the SVM parameters are freely available at http://chemlab.scu.edu.cn/ predict_SubNL/index.htm and the dataset used in our study is from Shen and Chou’s work by downloading at http://chou.med.harvard.edu/ bioinf/Nuc-PLoc/Data.htm

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Fast Fourier Transform-based Support Vector Machine for Prediction of G-protein Coupled Receptor Subfamilies

Cited by 16 publications

References 36 publications

On the hierarchical classification of G protein-coupled receptors

On the hierarchical classification of G protein-coupled receptors

Alignment-Independent Techniques for Protein Classification

Using position specific scoring matrix and auto covariance to predict protein subnuclear localization

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