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Classifying G-protein coupled receptors with support vector machines

R. Karchin, K. Karplus, D. Haussler
2002 Bioinformatics  
We discuss the relative merits of various automated methods for recognizing G-Protein Coupled Receptors (GPCRs), a superfamily of cell membrane proteins.  ...  We compare a simple nearest neighbor approach (BLAST), methods based on multiple alignments generated by a statistical profile Hidden Markov Model (HMM), and methods, including Support Vector Machines  ...  In our experiments, the data to be classified are protein sequences of the G-Protein Coupled Receptor (GPCR) superfamily (Watson and Arkinstall, 1994) .  ... 
doi:10.1093/bioinformatics/18.1.147 pmid:11836223 fatcat:atr3aktehrd7ldwxv7chyohg7y

Predicting the Coupling Specificity of G-protein Coupled Receptors to G-proteins by Support Vector Machines

Cui-Ping Guan, Zhen-Ran Jiang, Yan-Hong Zhou
2005 Genomics, Proteomics & Bioinformatics  
Based on these features, classif iers have been developed to predict the coupling specif icity of GPCRs to G-proteins using support vector machines.  ...  G-protein coupled receptors (GPCRs) represent one of the most important classes of drug targets for pharmaceutical industry and play important roles in cellular signal transduction.  ...  the coupling specificity based on these features and support vector machines (SVMs).  ... 
doi:10.1016/s1672-0229(05)03035-4 pmid:16689694 pmcid:PMC5173181 fatcat:hklnja4vlrcibap44rus4ftosa

A SVM for GPCR Protein Prediction Using Pattern Discovery

Francisco Nascimento Junior, Ing Ren Tsang, George D.C. Cavalcanti
2008 2008 Eighth International Conference on Hybrid Intelligent Systems  
G-protein coupled receptors (GPCRs) represent one of the largest protein families in Human Genome. Most of these receptors are major target for drug discovery and development.  ...  Vilo [2] proposed an algorithm in order to extract pattern of regular expressions from known protein GPCR sequences and used them to predict coupling specificity of G protein coupled receptors to their  ...  of the G protein coupled receptors to their G proteins.  ... 
doi:10.1109/his.2008.51 dblp:conf/his/JuniorTC08 fatcat:3hefg2v4tzfu5njfxjnog5jdoe

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

Yan-Zhi GUO, Meng-Long LI, Ke-Long WANG, Zhi-Ning WEN, Min-Chun LU, Li-Xia LIU, Lin JIANG
2005 Acta Biochimica et Biophysica Sinica  
Although the sequence information on G-protein coupled receptors (GPCRs) continues to grow, many GPCRs remain orphaned (i.e. ligand specificity unknown) or poorly characterized with little structural information  ...  In this study, a method of fast Fourier transform-based support vector machine has been developed for predicting GPCR subfamilies according to protein's hydrophobicity.  ...  Acknowledgement This work was supported by the State Key Laboratory of Chemo/Biosensing and Chemometrics, Hunan University (Changsha, China).  ... 
doi:10.1111/j.1745-7270.2005.00110.x pmid:16270155 fatcat:fzgs4m5ncrah3ahsaih2esivhm

A novel fractal approach for predicting G-protein–coupled receptors and their subfamilies with support vector machines

Guoping Nie, Yong Li, Feichi Wang, Siwen Wang, Xuehai Hu, Feng Liu, Dong-Hoon Lee, Ricardo Lagoa, Sandeep Kumar
2015 Bio-medical materials and engineering  
G-protein-coupled receptors (GPCRs) are seven membrane-spanning proteins and regulate many important physiological processes, such as vision, neurotransmission, immune response and so on.  ...  Four groups of features are considered, and each group is evaluated by support vector machine (SVM) and 10-fold cross-validation test.  ...  . / A novel fractal approach for predicting G-protein-coupled receptors and their subfamilies S1832 Support vector machine A support vector machine is one of the most important machine learning algorithms  ... 
doi:10.3233/bme-151485 pmid:26405954 fatcat:yqttnm32wvgupprxp3n6gkeoum

GRIFFIN: a system for predicting GPCR-G-protein coupling selectivity using a support vector machine and a hidden Markov model

Y. Yabuki, T. Muramatsu, T. Hirokawa, H. Mukai, M. Suwa
2005 Nucleic Acids Research  
We describe a novel system, GRIFFIN (G-protein and Receptor Interaction Feature Finding INstrument), that predicts G-protein coupled receptor (GPCR) and G-protein coupling selectivity based on a support  ...  vector machine (SVM) and a hidden Markov model (HMM) with high sensitivity and specificity.  ...  vector machines (SVMs) and statistical analysis.  ... 
doi:10.1093/nar/gki495 pmid:15980445 pmcid:PMC1160255 fatcat:p4ktqbpgx5eflljd6ql7jnp6nq

Development of Prediction Method for GPCR-G-protein Coupling Selectivity Using Amino Acid Properties

Yukimitsu Yabuki, Masami Ikeda, Yuri Mukai-Ikeda, Yoshihisa Ishida
2009 The Open Structural Biology Journal  
Some amino acid properties with high FR value were picked up as the effective characteristics for selecting G-protein type, and they were used as feature vectors in support vector machine (SVM) to predict  ...  We describe a novel method for predicting G-protein coupled receptor (GPCR) -G-protein coupling selectivity using amino acid properties of specific residues in GPCR sequences.  ...  Training and Testing for Predicting GPCR -G-protein Coupling Selectivity by using Support Vector Machines Support Vector Machine (SVM) was adapted to discriminate G-protein binding types by utilizing amino  ... 
doi:10.2174/1874199100903020149 fatcat:5f65wxtzknec3eizaakhf2q6uu

A robust hybrid between genetic algorithm and support vector machine for extracting an optimal feature gene subset

Li Li, Wei Jiang, Xia Li, Kathy L. Moser, Zheng Guo, Lei Du, Qiuju Wang, Eric J. Topol, Qing Wang, Shaoqi Rao
2005 Genomics  
We have formalized a robust gene selection approach based on a hybrid between genetic algorithm and support vector machine.  ...  The resulting classifier(s) (the optimal gene subset(s)) has achieved the highest accuracy (99%) for prediction of independent microarray samples in comparisons with marginal filters and a hybrid between  ...  Acknowledgments This work was supported in part by the National Natural Science Foundation of China (Grants 30170515 and 30370798), the National High Tech Development Project, the Chinese 863 Program (  ... 
doi:10.1016/j.ygeno.2004.09.007 pmid:15607418 fatcat:wlguq3kumjcvhgizssxw5eaxyu

Functional Classification of G-Protein Coupled Receptors, Based on Their Specific Ligand Coupling Patterns [chapter]

Burcu Bakir, Osman Ugur Sezerman
2006 Lecture Notes in Computer Science  
Functional identification of G-Protein Coupled Receptors (GPCRs) is one of the current focus areas of pharmaceutical research.  ...  and utilized for a Support Vector Machine (SVM)-based classification.  ...  Introduction G-Protein Coupled Receptors (GPCRs) are vital protein bundles with their key role in cellular signaling and regulation of various basic physiological processes.  ... 
doi:10.1007/11732242_1 fatcat:64yicxll7fbxjmy2fn4iolchui

SVM Model for Identification of human GPCRs [article]

Sonal Shrivastava, K. R. Pardasani, M. M. Malik
2010 arXiv   pre-print
G-protein coupled receptors (GPCRs) constitute a broad class of cell-surface receptors in eukaryotes and they possess seven transmembrane a-helical domains.  ...  Our method classifies Level 1 subfamilies of GPCRs with 94% accuracy.  ...  INTRODUCTION G-protein coupled receptors (GPCRs) are one of the largest superfamilies of membrane proteins in mammals.  ... 
arXiv:1002.3983v1 fatcat:4iqv4r73vbcx3hrwev45qiis34

Prediction of G Protein-Coupled Receptors with SVM-Prot Features and Random Forest

Zhijun Liao, Ying Ju, Quan Zou
2016 Scientifica  
G protein-coupled receptors (GPCRs) are the largest receptor superfamily. In this paper, we try to employ physical-chemical properties, which come from SVM-Prot, to represent GPCR.  ...  Random Forest was utilized as classifier for distinguishing them from other protein sequences. MEME suite was used to detect the most significant 10 conserved motifs of human GPCRs.  ...  Introduction The G protein-coupled receptors (GPCRs) are only discovered in eukaryotes, which constitute a vast protein family and perform their various functions always through coupling with G proteins  ... 
doi:10.1155/2016/8309253 pmid:27529053 pmcid:PMC4978840 fatcat:q5cdcorkbveklivog7iaoipfu4

Proposal of Pseudo Amino Acid Composition and Its Impacts and Profound Influence

Kuo-Chen Chou, Gordon Life Science Institute, Boston, Massachusetts 02478, United States of America
2021 Journal of Engineering and Applied Sciences Technology  
Xiao X, Wang P, Chou KC (2011) GPCR-2L: Predicting G pseudo amino acid composition and support vector machine protein-coupled receptors and their types by hybridizing two to predict protein structural  ...  Also, before 2001 the support-vector machines (SVMs) had been widely used to classify biological sequences.  ... 
doi:10.47363/jeast/2021(3)126 fatcat:ddqmbynsnncprgni7zjkv3ksgq

A Survey of Computational Intelligence Techniques in Protein Function Prediction

Arvind Kumar Tiwari, Rajeev Srivastava
2014 International Journal of Proteomics  
coupled receptors, membrane proteins, and pathway analysis from gene expression datasets.  ...  The summary shows that ensemble classifiers and integration of multiple heterogeneous data are useful for protein function prediction.  ...  of nuclear and G-protein coupled receptors families and their subfamilies.  ... 
doi:10.1155/2014/845479 pmid:25574395 pmcid:PMC4276698 fatcat:p3vwanr2nran7arwzotirhgyte

Prediction of G-protein-coupled receptor classes based on the concept of Chou's pseudo amino acid composition: An approach from discrete wavelet transform

Jian-Ding Qiu, Jian-Hua Huang, Ru-Ping Liang, Xiao-Quan Lu
2009 Analytical Biochemistry  
a b s t r a c t 22 Being the largest family of cell surface receptors, G-protein-coupled receptors (GPCRs) are among the 23 most frequent targets.  ...  In this study, the decomposition level 3 was 186 chosen to classify the GPCRs, and the obtained 16 dimension fea-187 ture vectors were then inputted to SVM for classification. 188 Support vector machine  ... 
doi:10.1016/j.ab.2009.04.009 pmid:19364489 fatcat:drjrnic52baz7o5d63gtfdmg4q

Risk gene identification and support vector machine learning to construct an early diagnosis model of myocardial infarction

Hong‑Zhi Fang, Dan‑Li Hu, Qin Li, Su Tu
2020 Molecular Medicine Reports  
The present study aimed to identify genes associated with increased risk of myocardial infarction (MI) and construct an early diagnosis model based on support vector machine (SVM) learning.  ...  In total, 87 genes were selected as candidate genes, and were primarily enriched in functions including 'Gprotein coupled receptor signaling' or pathways such as 'focal adhesion'.  ...  Support vector machine (SVM) is a supervised learning model used for classification and regression analysis (9) .  ... 
doi:10.3892/mmr.2020.11247 pmid:32705275 pmcid:PMC7411293 fatcat:aonhwkxbefd4hfqq4kjebhaqoq
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