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Volumn 16, Issue 1, 2015, Pages

Label noise in subtype discrimination of class C G protein-coupled receptors: A systematic approach to the analysis of classification errors

Author keywords

G Protein coupled receptors; Label noise; Phylogenetic trees; Support vector machines

Indexed keywords

BIOINFORMATICS; BIOLOGICAL MEMBRANES; C (PROGRAMMING LANGUAGE); CHEMICAL ANALYSIS; CYTOLOGY; MOLECULAR BIOLOGY; NOISE POLLUTION; SUPPORT VECTOR MACHINES; SYSTEMATIC ERRORS;

EID: 84942521494     PISSN: None     EISSN: 14712105     Source Type: Journal    
DOI: 10.1186/s12859-015-0731-9     Document Type: Article
Times cited : (9)

References (60)
  • 1
    • 84899651693 scopus 로고    scopus 로고
    • Classification in the presence of label noise: a survey
    • Frénay B, Verleysen M. Classification in the presence of label noise: a survey. IEEE Trans Neural Netw Learn Syst. 2014; 25(5):845-69.
    • (2014) IEEE Trans Neural Netw Learn Syst , vol.25 , Issue.5 , pp. 845-869
    • Frénay, B.1    Verleysen, M.2
  • 2
    • 80054896328 scopus 로고    scopus 로고
    • Computational Intelligence in biomedicine: Some contributions
    • Verleysen M, editor. Procs. of the 18th European Symposium on Artificial Neural Networks (ESANN 2010). Bruges, Belgium: d-side pub.
    • Lisboa PJG, Vellido A, Martín JD. Computational Intelligence in biomedicine: Some contributions. In: Verleysen M, editor. Procs. of the 18th European Symposium on Artificial Neural Networks (ESANN 2010). Bruges, Belgium: d-side pub.: 2010. p. 429-38.
    • (2010)
    • Lisboa, P.J.G.1    Vellido, A.2    Martín, J.D.3
  • 3
    • 80052422950 scopus 로고    scopus 로고
    • Label noise-tolerant hidden Markov models for segmentation: application to ECGs
    • Gunopulos D, et al, editors. Machine Learning and Knowledge Discovery in Databases. Heidelberg, LNCS 6911: Springer
    • Frénay B, de Lannoy G, Verleysen M. Label noise-tolerant hidden Markov models for segmentation: application to ECGs. In: Gunopulos D, et al, editors. Machine Learning and Knowledge Discovery in Databases. Heidelberg, LNCS 6911: Springer. p. 455-70.
    • Frénay, B.1    de Lannoy, G.2    Verleysen, M.3
  • 5
    • 78650608807 scopus 로고    scopus 로고
    • International expert panel on inflammatory breast cancer: consensus statement for standardized diagnosis and treatment
    • Dawood S, Merajver SD, Viens P, Vermeulen PB, Swain SM, Buchholz TA, et al. International expert panel on inflammatory breast cancer: consensus statement for standardized diagnosis and treatment. Ann Oncol. 2011; 22(3):515-23.
    • (2011) Ann Oncol , vol.22 , Issue.3 , pp. 515-523
    • Dawood, S.1    Merajver, S.D.2    Viens, P.3    Vermeulen, P.B.4    Swain, S.M.5    Buchholz, T.A.6
  • 7
    • 84884286007 scopus 로고    scopus 로고
    • Classifying G-Protein-Coupled Receptors to the finest subtype level
    • Gao QB, Ye XF, He J. Classifying G-Protein-Coupled Receptors to the finest subtype level. Biochem Biophys Res Commun. 2013; 439(2):303-8.
    • (2013) Biochem Biophys Res Commun , vol.439 , Issue.2 , pp. 303-308
    • Gao, Q.B.1    Ye, X.F.2    He, J.3
  • 8
    • 84887999195 scopus 로고    scopus 로고
    • An overview of the diverse roles of G-protein coupled receptors (GPCRs) in the pathophysiology of various human diseases
    • Heng BC, Aubel D, Fussenegger M. An overview of the diverse roles of G-protein coupled receptors (GPCRs) in the pathophysiology of various human diseases. Biotechnol Adv. 2013; 31(8):1676-94.
    • (2013) Biotechnol Adv , vol.31 , Issue.8 , pp. 1676-1694
    • Heng, B.C.1    Aubel, D.2    Fussenegger, M.3
  • 10
    • 0038662595 scopus 로고    scopus 로고
    • Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors
    • Pin JP, Galvez T, Prezeau L. Evolution, structure, and activation mechanism of family 3/C G-protein-coupled receptors. Pharmacol Ther. 2003; 98(3):325-54.
    • (2003) Pharmacol Ther , vol.98 , Issue.3 , pp. 325-354
    • Pin, J.P.1    Galvez, T.2    Prezeau, L.3
  • 11
    • 84906827756 scopus 로고    scopus 로고
    • Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs for Treating CNS Disorders
    • Conn PJ, Lindsley CW, Meiler J, Niswender CM. Opportunities and Challenges in the Discovery of Allosteric Modulators of GPCRs for Treating CNS Disorders. Nat Rev Drug Discov. 2014; 13(9):692-708.
    • (2014) Nat Rev Drug Discov , vol.13 , Issue.9 , pp. 692-708
    • Conn, P.J.1    Lindsley, C.W.2    Meiler, J.3    Niswender, C.M.4
  • 16
    • 84872221774 scopus 로고    scopus 로고
    • Structure-function of the G Protein-Coupled Receptor superfamily
    • Katritch V, Cherezov V, Stevens RC. Structure-function of the G Protein-Coupled Receptor superfamily. Annu Rev Pharmacol Toxicol. 2013; 53:531-56.
    • (2013) Annu Rev Pharmacol Toxicol , vol.53 , pp. 531-556
    • Katritch, V.1    Cherezov, V.2    Stevens, R.C.3
  • 17
    • 84897580006 scopus 로고    scopus 로고
    • Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator
    • Wu H, Wang C, Gregory KJ, Han GW, Cho KP, Xia Y, et al. Structure of a class C GPCR metabotropic glutamate receptor 1 bound to an allosteric modulator. Sci. 2014; 344(6179):58-64.
    • (2014) Sci , vol.344 , Issue.6179 , pp. 58-64
    • Wu, H.1    Wang, C.2    Gregory, K.J.3    Han, G.W.4    Cho, K.P.5    Xia, Y.6
  • 18
    • 84904994581 scopus 로고    scopus 로고
    • Structure of class C GPCR metabotropic glutamate receptor 5 transmembrane domain
    • Doré AS, Okrasa K, Patel JC, Serrano-Vega M, Bennett K, Cooke RM, et al. Structure of class C GPCR metabotropic glutamate receptor 5 transmembrane domain. Nature. 2014; 551:557-62.
    • (2014) Nature , vol.551 , pp. 557-562
    • Doré, A.S.1    Okrasa, K.2    Patel, J.C.3    Serrano-Vega, M.4    Bennett, K.5    Cooke, R.M.6
  • 20
    • 33750736013 scopus 로고    scopus 로고
    • The accuracy of several multiple sequence alignment programs for proteins
    • Nuin PA, Wang Z, Tillier ER. The accuracy of several multiple sequence alignment programs for proteins. BMC Bioinforma. 2006; 7(1):471.
    • (2006) BMC Bioinforma , vol.7 , Issue.1 , pp. 471
    • Nuin, P.A.1    Wang, Z.2    Tillier, E.R.3
  • 21
    • 34147130792 scopus 로고    scopus 로고
    • An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences
    • Ye K, Kosters WA, IJzerman AP. An efficient, versatile and scalable pattern growth approach to mine frequent patterns in unaligned protein sequences. Bioinformatics. 2007; 23(6):687-93.
    • (2007) Bioinformatics , vol.23 , Issue.6 , pp. 687-693
    • Ye, K.1    Kosters, W.A.2    IJzerman, A.P.3
  • 22
    • 84867004398 scopus 로고    scopus 로고
    • Using amino acid Physicochemical Distance Transformation for fast protein remote homology detection
    • Liu B, Wang X, Chen Q, Dong Q, Lan X. Using amino acid Physicochemical Distance Transformation for fast protein remote homology detection. PLoS ONE. 2012; 7(9):e46633.
    • (2012) PLoS ONE , vol.7 , Issue.9 , pp. e46633
    • Liu, B.1    Wang, X.2    Chen, Q.3    Dong, Q.4    Lan, X.5
  • 24
    • 0035664149 scopus 로고    scopus 로고
    • Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification
    • Rehm B. Bioinformatic tools for DNA/protein sequence analysis, functional assignment of genes and protein classification. Appl Microbiol Biotechnol. 2001; 57(5-6):579-92.
    • (2001) Appl Microbiol Biotechnol , vol.57 , Issue.5-6 , pp. 579-592
    • Rehm, B.1
  • 25
    • 79953093817 scopus 로고    scopus 로고
    • A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models
    • Bernardes JS, Carbone A, Zaverucha G. A discriminative method for family-based protein remote homology detection that combines inductive logic programming and propositional models. BMC Bioinforma. 2011; 12:83.
    • (2011) BMC Bioinforma , vol.12 , pp. 83
    • Bernardes, J.S.1    Carbone, A.2    Zaverucha, G.3
  • 27
    • 84899539529 scopus 로고    scopus 로고
    • Protein sequence classification with improved Extreme Learning Machine algorithms
    • Cao J, Xiong L. Protein sequence classification with improved Extreme Learning Machine algorithms. BioMed Res Int. 2014;2014: ID103054.
    • (2014) BioMed Res Int. 2014
    • Cao, J.1    Xiong, L.2
  • 28
    • 0027215340 scopus 로고
    • DNA and peptide sequences and chemical processes multivariately modelled by Principal Component Analysis and Partial Least-Squares projections to latent structures
    • Wold S, Jonsson J, Sjöström M, Sandberg M, Rännar S. DNA and peptide sequences and chemical processes multivariately modelled by Principal Component Analysis and Partial Least-Squares projections to latent structures. Anal Chim Acta. 1993; 277:239-53.
    • (1993) Anal Chim Acta , vol.277 , pp. 239-253
    • Wold, S.1    Jonsson, J.2    Sjöström, M.3    Sandberg, M.4    Rännar, S.5
  • 29
    • 0036130770 scopus 로고    scopus 로고
    • Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences
    • Lapinsh M, Gutcaits A, Prusis P, Post C, Lundstedt T, Wikberg JES. Classification of G-protein coupled receptors by alignment-independent extraction of principal chemical properties of primary amino acid sequences. Protein Sci. 2002; 11(4):795-805.
    • (2002) Protein Sci , vol.11 , Issue.4 , pp. 795-805
    • Lapinsh, M.1    Gutcaits, A.2    Prusis, P.3    Post, C.4    Lundstedt, T.5    Wikberg, J.E.S.6
  • 30
    • 0032474777 scopus 로고    scopus 로고
    • New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids
    • Sandberg M, Eriksson L, Jonsson J, Sjöström M, Wold S. New chemical descriptors relevant for the design of biologically active peptides. A multivariate characterization of 87 amino acids. J Med Chem. 1998; 41(14):2481-91.
    • (1998) J Med Chem , vol.41 , Issue.14 , pp. 2481-2491
    • Sandberg, M.1    Eriksson, L.2    Jonsson, J.3    Sjöström, M.4    Wold, S.5
  • 32
    • 84925491224 scopus 로고    scopus 로고
    • The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors
    • Cruz-Barbosa R, Vellido A, Giraldo J. The influence of alignment-free sequence representations on the semi-supervised classification of class C G protein-coupled receptors. Med Biol Eng Comput. 2015; 53(2):137-49.
    • (2015) Med Biol Eng Comput , vol.53 , Issue.2 , pp. 137-149
    • Cruz-Barbosa, R.1    Vellido, A.2    Giraldo, J.3
  • 33
    • 40749093745 scopus 로고    scopus 로고
    • SVM-HUSTLE-an iterative semi-supervised machine learning approach for pairwise protein remote homology detection
    • Shah AR, Oehmen CS, Webb-Robertson BJ. SVM-HUSTLE-an iterative semi-supervised machine learning approach for pairwise protein remote homology detection. Bioinformatics. 2008; 4:783-90.
    • (2008) Bioinformatics , vol.4 , pp. 783-790
    • Shah, A.R.1    Oehmen, C.S.2    Webb-Robertson, B.J.3
  • 34
    • 0344033670 scopus 로고    scopus 로고
    • Efficient remote homology detection using local structure
    • Hou Y, Hsu W, Lee ML, Bystroff C. Efficient remote homology detection using local structure. Bioinformatics. 2003; 19:2294-301.
    • (2003) Bioinformatics , vol.19 , pp. 2294-2301
    • Hou, Y.1    Hsu, W.2    Lee, M.L.3    Bystroff, C.4
  • 35
    • 33845467978 scopus 로고    scopus 로고
    • A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets
    • Ogul H, Mumcuoglu EU. A discriminative method for remote homology detection based on n-peptide compositions with reduced amino acid alphabets. BioSystems. 2007; 87:75-81.
    • (2007) BioSystems , vol.87 , pp. 75-81
    • Ogul, H.1    Mumcuoglu, E.U.2
  • 36
    • 28044469042 scopus 로고    scopus 로고
    • SVM-BALSA: Remote homology detection based on Bayesian sequence alignment
    • Webb-Robertson BJ, Oehmen C, Matzke M. SVM-BALSA: Remote homology detection based on Bayesian sequence alignment. Comput Biol Chem. 2005; 29:440-3.
    • (2005) Comput Biol Chem , vol.29 , pp. 440-443
    • Webb-Robertson, B.J.1    Oehmen, C.2    Matzke, M.3
  • 37
    • 0036166451 scopus 로고    scopus 로고
    • Classifying G-protein coupled receptors with support vector machines
    • Karchin R, Karplus K, Haussler D. Classifying G-protein coupled receptors with support vector machines. Bioinformatics. 2002; 18(1):147-59.
    • (2002) Bioinformatics , vol.18 , Issue.1 , pp. 147-159
    • Karchin, R.1    Karplus, K.2    Haussler, D.3
  • 38
    • 0003991806 scopus 로고    scopus 로고
    • Statistical Learning Theory
    • New York: John Wiley & Sons
    • Vapnik VN. Statistical Learning Theory. New York: John Wiley & Sons; 1998.
    • (1998)
    • Vapnik, V.N.1
  • 39
    • 34249753618 scopus 로고
    • Support vector networks
    • Cortes C, Vapnik VN. Support vector networks. Mach Learn. 1995; 20(3):273-97.
    • (1995) Mach Learn , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.N.2
  • 41
    • 0000874557 scopus 로고
    • Theoretical foundations of the potential function method in pattern recognition learning
    • Aizerman A, Braverman EM, Rozoner LI. Theoretical foundations of the potential function method in pattern recognition learning. Autom Remote Control. 1964; 25:821-37.
    • (1964) Autom Remote Control , vol.25 , pp. 821-837
    • Aizerman, A.1    Braverman, E.M.2    Rozoner, L.I.3
  • 42
    • 79955702502 scopus 로고    scopus 로고
    • LIBSVM: A library for Support Vector Machines
    • Chang C, Lin C. LIBSVM: A library for Support Vector Machines. ACM Trans Intell Syst Technol. 2011; 2(3):27.
    • (2011) ACM Trans Intell Syst Technol , vol.2 , Issue.3 , pp. 27
    • Chang, C.1    Lin, C.2
  • 43
    • 0016772212 scopus 로고
    • Comparison of the predicted and observed secondary structure of T4 phage lysozyme
    • Matthews BW. Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochim Biophys Acta Protein Struct Mol Enzymol. 1975; 405(2):442-51.
    • (1975) Biochim Biophys Acta Protein Struct Mol Enzymol , vol.405 , Issue.2 , pp. 442-451
    • Matthews, B.W.1
  • 44
    • 84856050251 scopus 로고    scopus 로고
    • Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context
    • Martinez J, Carroll RJ, Müller S, Sampson JN, Chatterjee N. Empirical Performance of Cross-Validation With Oracle Methods in a Genomics Context. The Am Stat. 2011; 65(4):223-8.
    • (2011) The Am Stat , vol.65 , Issue.4 , pp. 223-228
    • Martinez, J.1    Carroll, R.J.2    Müller, S.3    Sampson, J.N.4    Chatterjee, N.5
  • 45
    • 84918777103 scopus 로고    scopus 로고
    • Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information
    • Jayawardana K, Schramm S, Haydu L, Thompson JF, Scolye RA, Mann G, et al. Determination of prognosis in metastatic melanoma through integration of clinico-pathologic, mutation, mRNA, microRNA, and protein information. Int J Cancer. 2015; 136(4):863-74.
    • (2015) Int J Cancer , vol.136 , Issue.4 , pp. 863-874
    • Jayawardana, K.1    Schramm, S.2    Haydu, L.3    Thompson, J.F.4    Scolye, R.A.5    Mann, G.6
  • 46
    • 67650742236 scopus 로고    scopus 로고
    • Treevolution: visual analysis of phylogenetic trees
    • Santamaría R, Therón R. Treevolution: visual analysis of phylogenetic trees. Bioinformatics. 2009; 25(15):1970-1.
    • (2009) Bioinformatics , vol.25 , Issue.15 , pp. 1970-1971
    • Santamaría, R.1    Therón, R.2
  • 47
    • 80054078476 scopus 로고    scopus 로고
    • Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega
    • Sievers F, et al. Fast, scalable generation of high-quality protein multiple sequence alignments using Clustal Omega. Mol Syst Biol. 2011; 7:539.
    • (2011) Mol Syst Biol , vol.7 , pp. 539
    • Sievers, F.1
  • 48
    • 0023084055 scopus 로고
    • Progressive sequence alignment as a prerequisite to correct phylogenetic trees
    • Feng DF, Doolittle RF. Progressive sequence alignment as a prerequisite to correct phylogenetic trees. J Mol Evol. 1987; 25(4):351-60.
    • (1987) J Mol Evol , vol.25 , Issue.4 , pp. 351-360
    • Feng, D.F.1    Doolittle, R.F.2
  • 50
    • 33847341188 scopus 로고    scopus 로고
    • Protein family classification with partial least squares
    • Opiyo SO, Moriyama EN. Protein family classification with partial least squares. J Proteome Res. 2007; 6(2):846-53.
    • (2007) J Proteome Res , vol.6 , Issue.2 , pp. 846-853
    • Opiyo, S.O.1    Moriyama, E.N.2
  • 51
    • 84898030282 scopus 로고    scopus 로고
    • A study of the effect of different types of noise on the precision of supervised learning techniques
    • Nettleton D, Orriols-Puig A, Fornells A. A study of the effect of different types of noise on the precision of supervised learning techniques. Artif Intell Rev. 2010; 33(4):275-306.
    • (2010) Artif Intell Rev , vol.33 , Issue.4 , pp. 275-306
    • Nettleton, D.1    Orriols-Puig, A.2    Fornells, A.3
  • 52
    • 80053403826 scopus 로고    scopus 로고
    • Ensemble methods in machine learning
    • Kittler J, Roli F, editors. Multiple Classifier Systems. Heidelberg: Springer. Lecture Notes in Computer Science
    • Dietterich TG. Ensemble methods in machine learning. In: Kittler J, Roli F, editors. Multiple Classifier Systems. Heidelberg: Springer. Lecture Notes in Computer Science, Vol. 1857; 2000. p. 1-15.
    • (2000) , vol.1857 , pp. 1-15
    • Dietterich, T.G.1
  • 53
    • 0000046054 scopus 로고    scopus 로고
    • Identifying mislabeled training data
    • Brodley CE, Friedl MA. Identifying mislabeled training data. J Artif Intell Res. 1999; 11:131-67.
    • (1999) J Artif Intell Res , vol.11 , pp. 131-167
    • Brodley, C.E.1    Friedl, M.A.2
  • 54
    • 84893677824 scopus 로고    scopus 로고
    • Ensemble-based noise detection: noise ranking and visual performance evaluation
    • Sluban B, Lavrac N, Gamberger D. Ensemble-based noise detection: noise ranking and visual performance evaluation. Data Min Knowl Discov. 2014; 28:265-303.
    • (2014) Data Min Knowl Discov , vol.28 , pp. 265-303
    • Sluban, B.1    Lavrac, N.2    Gamberger, D.3
  • 58
    • 84884965072 scopus 로고    scopus 로고
    • Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition
    • Sáez JA, Galar M, Luengo J, Herrera F. Analyzing the presence of noise in multi-class problems: alleviating its influence with the One-vs-One decomposition. Knowl Inf Syst. 2014; 38(1):179-206.
    • (2014) Knowl Inf Syst , vol.38 , Issue.1 , pp. 179-206
    • Sáez, J.A.1    Galar, M.2    Luengo, J.3    Herrera, F.4
  • 59
    • 65649138430 scopus 로고    scopus 로고
    • A systematic analysis of performance measures for classification tasks
    • Sokolova M, Lapalme G. A systematic analysis of performance measures for classification tasks. Inf Process Manag. 2009; 45(4):427-37.
    • (2009) Inf Process Manag , vol.45 , Issue.4 , pp. 427-437
    • Sokolova, M.1    Lapalme, G.2
  • 60
    • 84864668842 scopus 로고    scopus 로고
    • A Comparison of MCC and CEN Error Measures in Multi-Class Prediction
    • Jurman G, Riccadonna S, Furlanello C. A Comparison of MCC and CEN Error Measures in Multi-Class Prediction. PLoS ONE. 2012; 7(8):e41882.
    • (2012) PLoS ONE , vol.7 , Issue.8 , pp. e41882
    • Jurman, G.1    Riccadonna, S.2    Furlanello, C.3


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