메뉴 건너뛰기




Volumn 9, Issue 5, 2008, Pages 392-403

Penalized feature selection and classification in bioinformatics

Author keywords

Bioinformatics application; Feature selection; Penalization

Indexed keywords

BIOLOGICAL MARKER; DNA;

EID: 49949090353     PISSN: 14675463     EISSN: 14774054     Source Type: Journal    
DOI: 10.1093/bib/bbn027     Document Type: Review
Times cited : (209)

References (63)
  • 3
    • 33646383088 scopus 로고    scopus 로고
    • Dimension reduction for classification with gene expression microarray data
    • Dai J, Lieu L, Rocke D. Dimension reduction for classification with gene expression microarray data. Stat Appl Genet Mol Biol 2006;5 6.
    • (2006) Stat Appl Genet Mol Biol , vol.5 , pp. 6
    • Dai, J.1    Lieu, L.2    Rocke, D.3
  • 4
    • 33745561205 scopus 로고    scopus 로고
    • An introduction to variable and feature selection
    • Guyon I, Elisseeff A. An introduction to variable and feature selection. J Mach Learn Res 2003;3:1157-82.
    • (2003) J Mach Learn Res , vol.3 , pp. 1157-1182
    • Guyon, I.1    Elisseeff, A.2
  • 5
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys Y, Inza I, Larranaga P. A review of feature selection techniques in bioinformatics. Bioinformatics 2007;19: 2507-17.
    • (2007) Bioinformatics , vol.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.2    Larranaga, P.3
  • 7
    • 0036166439 scopus 로고    scopus 로고
    • Tumor classification by partial least squares using microarray gene expression data
    • Nguyen DV, Rocke DM. Tumor classification by partial least squares using microarray gene expression data. Bioinformatics 2002;18 39-50.
    • (2002) Bioinformatics , vol.18 , pp. 39-50
    • Nguyen, D.V.1    Rocke, D.M.2
  • 8
    • 0031334221 scopus 로고    scopus 로고
    • Selection of relevant features and examples in machine learning
    • Blum A, Langley P. Selection of relevant features and examples in machine learning. Artif Intell 1997;97:245-71.
    • (1997) Artif Intell , vol.97 , pp. 245-271
    • Blum, A.1    Langley, P.2
  • 9
    • 27744565003 scopus 로고    scopus 로고
    • Classification and selection and biomarkers in genomic data using Lasso
    • Ghosh D, Chinnaiyan AM. Classification and selection and biomarkers in genomic data using Lasso. J Biomed Biotechnol 2005;2:147-54.
    • (2005) J Biomed Biotechnol , vol.2 , pp. 147-154
    • Ghosh, D.1    Chinnaiyan, A.M.2
  • 10
    • 30344438839 scopus 로고    scopus 로고
    • Gene selection using support vector machines with non-convex penalty
    • Zhang H, Ahn J, Lin X, et al. Gene selection using support vector machines with non-convex penalty. Bioinformatics 2006;22 88-95.
    • (2006) Bioinformatics , vol.22 , pp. 88-95
    • Zhang, H.1    Ahn, J.2    Lin, X.3
  • 11
    • 33847007697 scopus 로고    scopus 로고
    • Sparse logistic regression with Lp penalty for biomarker identification
    • Liu Z, Jiang F, Tian G, et al. Sparse logistic regression with Lp penalty for biomarker identification. Stat Appl Genet Mol Biol 2007;6:6.
    • (2007) Stat Appl Genet Mol Biol , vol.6 , pp. 6
    • Liu, Z.1    Jiang, F.2    Tian, G.3
  • 12
    • 0033569406 scopus 로고    scopus 로고
    • Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring
    • Golub TR, Slonim DK, Tamayo, P, et al. Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science 1999;286:531-7.
    • (1999) Science , vol.286 , pp. 531-537
    • Golub, T.R.1    Slonim, D.K.2    Tamayo, P.3
  • 13
    • 0035949684 scopus 로고    scopus 로고
    • Predicting the clinical status of human breast cancer by using gene expression profiles
    • West M, Blanchette C, Dressmna H, et al. Predicting the clinical status of human breast cancer by using gene expression profiles. Proc Natl Acad Sci USA 2001;98: 11462-67.
    • (2001) Proc Natl Acad Sci USA , vol.98 , pp. 11462-11467
    • West, M.1    Blanchette, C.2    Dressmna, H.3
  • 14
    • 3543081720 scopus 로고    scopus 로고
    • A simple epigenetic method for the diagnosis and classification of brain tumors
    • Zukiel R, Nowak S, Barciszewska A, et al. A simple epigenetic method for the diagnosis and classification of brain tumors. Mol Cancer Res 2004;2:196-202.
    • (2004) Mol Cancer Res , vol.2 , pp. 196-202
    • Zukiel, R.1    Nowak, S.2    Barciszewska, A.3
  • 15
    • 0036327616 scopus 로고    scopus 로고
    • Cellular vitamins, DNA methylation and cancer risk
    • Piyathilake C, Johannig GL. Cellular vitamins, DNA methylation and cancer risk. Am Soc Nutri Sci 2002;132: 2340S-22344S.
    • (2002) Am Soc Nutri Sci , vol.132
    • Piyathilake, C.1    Johannig, G.L.2
  • 16
    • 10244279272 scopus 로고    scopus 로고
    • Sample classification from protein mass spectrometry, by 'peak probability contrasts'
    • Tibshirani R, Hastie T, Narasimhan B, Soltys S, et al. Sample classification from protein mass spectrometry, by 'peak probability contrasts'. Bioinformatics 2004;20: 3034-44.
    • (2004) Bioinformatics , vol.20 , pp. 3034-3044
    • Tibshirani, R.1    Hastie, T.2    Narasimhan, B.3    Soltys, S.4
  • 17
    • 15744403951 scopus 로고    scopus 로고
    • Diagnosis of ovarian cancer based on mass spectra of blood samples
    • Man, and Cybernetics. The Hague, The Netherlands
    • Yang H, Mukomel Y, Fink E. Diagnosis of ovarian cancer based on mass spectra of blood samples. In: Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics. The Hague, The Netherlands, 2004;3444-50.
    • (2004) Proceedings of the IEEE International Conference on Systems , pp. 3444-3450
    • Yang, H.1    Mukomel, Y.2    Fink, E.3
  • 18
    • 12944272015 scopus 로고    scopus 로고
    • Plasma protein profiling by mass spectrometry for cancer diagnosis: Opportunities and limitations
    • Diamandis EP, van der Merwe DE. Plasma protein profiling by mass spectrometry for cancer diagnosis: Opportunities and limitations. Clin Cancer Res 2005;11:963-5.
    • (2005) Clin Cancer Res , vol.11 , pp. 963-965
    • Diamandis, E.P.1    van der Merwe, D.E.2
  • 19
    • 1542714925 scopus 로고    scopus 로고
    • Mismatch string kernels for discriminative protein classification
    • Leslie CS, Eskin E, Cohen A, et al. Mismatch string kernels for discriminative protein classification. Bioinformatics 2004; 20 467-76.
    • (2004) Bioinformatics , vol.20 , pp. 467-476
    • Leslie, C.S.1    Eskin, E.2    Cohen, A.3
  • 20
    • 35048818988 scopus 로고    scopus 로고
    • Semi-supervised protein classification using cluster kernels
    • Weston J, Leslie C, Zhou D, et al. Semi-supervised protein classification using cluster kernels. Adv Neural Inf Process Syst 2004;16:595-602.
    • (2004) Adv Neural Inf Process Syst , vol.16 , pp. 595-602
    • Weston, J.1    Leslie, C.2    Zhou, D.3
  • 21
    • 39049181470 scopus 로고    scopus 로고
    • A SVM score for more sensitive and reliable peptide identification via tandem mass spectrometry
    • Wang H, Fu Y, Sun R, et al. A SVM score for more sensitive and reliable peptide identification via tandem mass spectrometry. Pac Symp Biocomput 2006;11:303-14.
    • (2006) Pac Symp Biocomput , vol.11 , pp. 303-314
    • Wang, H.1    Fu, Y.2    Sun, R.3
  • 22
    • 0141515750 scopus 로고    scopus 로고
    • Prediction of protein subcellular locations by support vector machines using compositions of amino acid and amino acid pairs
    • Park KJ, Kanehisa M. Prediction of protein subcellular locations by support vector machines using compositions of amino acid and amino acid pairs. Bioinformatics 2003;19: 1656-63.
    • (2003) Bioinformatics , vol.19 , pp. 1656-1663
    • Park, K.J.1    Kanehisa, M.2
  • 23
    • 29144502189 scopus 로고    scopus 로고
    • Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria
    • Rey S, Gardy JL, Brinkman FSL. Assessing the precision of high-throughput computational and laboratory approaches for the genome-wide identification of protein subcellular localization in bacteria. BMC Genomics 2005;6:162.
    • (2005) BMC Genomics , vol.6 , pp. 162
    • Rey, S.1    Gardy, J.L.2    Brinkman, F.S.L.3
  • 24
    • 33746218840 scopus 로고    scopus 로고
    • Prediction of protein subcellular localization
    • Yu CS, Chen YC, Lu CH, et al. Prediction of protein subcellular localization. Proteins 2006:64:643-51.
    • (2006) Proteins , vol.64 , pp. 643-651
    • Yu, C.S.1    Chen, Y.C.2    Lu, C.H.3
  • 27
    • 15944363312 scopus 로고    scopus 로고
    • Classification of gene microarrays by penalized logistic regression
    • Zhu J, Hastie T. Classification of gene microarrays by penalized logistic regression. Biostatistics 2004;5:427-43.
    • (2004) Biostatistics , vol.5 , pp. 427-443
    • Zhu, J.1    Hastie, T.2
  • 28
    • 22944456563 scopus 로고    scopus 로고
    • Shen L, Tan EC. Dimension reduction based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans Comput Biol Bioinform 2005;2:166-75.
    • Shen L, Tan EC. Dimension reduction based penalized logistic regression for cancer classification using microarray data. IEEE/ACM Trans Comput Biol Bioinform 2005;2:166-75.
  • 29
    • 0345327592 scopus 로고    scopus 로고
    • A simple and efficient algorithm for gene selecting using sparse logistic regression
    • Shevade SK, Keerthi SS. A simple and efficient algorithm for gene selecting using sparse logistic regression. Bioinformatics 2003; 19:2246-53.
    • (2003) Bioinformatics , vol.19 , pp. 2246-2253
    • Shevade, S.K.1    Keerthi, S.S.2
  • 30
    • 0036489046 scopus 로고    scopus 로고
    • Comparison of discrimination methods for the classification of tumors using gene expression data
    • Dudoit S, Fridlyand J, Speed TP, et al. Comparison of discrimination methods for the classification of tumors using gene expression data. J Am Stat Assoc 2002;97:77-87.
    • (2002) J Am Stat Assoc , vol.97 , pp. 77-87
    • Dudoit, S.1    Fridlyand, J.2    Speed, T.P.3
  • 32
    • 28944437658 scopus 로고    scopus 로고
    • Regularized ROC method for disease classification and biomarker selection with microarray data
    • Ma S, Huang J. Regularized ROC method for disease classification and biomarker selection with microarray data. Bioinformatics 2005; 21:4356-62.
    • (2005) Bioinformatics , vol.21 , pp. 4356-4362
    • Ma, S.1    Huang, J.2
  • 33
    • 33746363482 scopus 로고    scopus 로고
    • Regularized binormal ROC method in disease classification using microarray data
    • Ma S, Song X, Huang J. Regularized binormal ROC method in disease classification using microarray data. BMC Bioinformatics 2006;7 253.
    • (2006) BMC Bioinformatics , vol.7 , pp. 253
    • Ma, S.1    Song, X.2    Huang, J.3
  • 34
    • 0032943941 scopus 로고    scopus 로고
    • Three-way ROCs
    • Mossman D. Three-way ROCs. Med Decis Making 1999;19: 78-89.
    • (1999) Med Decis Making , vol.19 , pp. 78-89
    • Mossman, D.1
  • 35
    • 0042346121 scopus 로고    scopus 로고
    • Tree induction for probability based rankings
    • Provost F, Domingos P. Tree induction for probability based rankings. Mach Learn 2003;52:199-215.
    • (2003) Mach Learn , vol.52 , pp. 199-215
    • Provost, F.1    Domingos, P.2
  • 37
    • 0036161259 scopus 로고    scopus 로고
    • Gene selection for cancer classification using support vector machine
    • Guyon I, Weston J, Barnhill S, et al. Gene selection for cancer classification using support vector machine. Mach Learn 2004;46 389-422.
    • (2004) Mach Learn , vol.46 , pp. 389-422
    • Guyon, I.1    Weston, J.2    Barnhill, S.3
  • 38
    • 0034843744 scopus 로고    scopus 로고
    • Support vector machine approach for protein subcellular localization prediction
    • Hua S, Sun Z. Support vector machine approach for protein subcellular localization prediction. Bioinformatics 2001;17: 721-8.
    • (2001) Bioinformatics , vol.17 , pp. 721-728
    • Hua, S.1    Sun, Z.2
  • 39
    • 1542559402 scopus 로고    scopus 로고
    • Support vector machine applications in computational biology
    • Scholkopf B, Tsuda K, Vert J, eds, MIT Press
    • Noble WS. Support vector machine applications in computational biology. In: Scholkopf B, Tsuda K, Vert J, (eds). Kernel Methods in Computational Biology. MIT Press, 2004, pp. 71-92.
    • (2004) Kernel Methods in Computational Biology , pp. 71-92
    • Noble, W.S.1
  • 41
    • 0001287271 scopus 로고    scopus 로고
    • Regression shrinkage and selection via the Lasso
    • Tibshirani R. Regression shrinkage and selection via the Lasso. JRSSB 1996;58:267-88.
    • (1996) JRSSB , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 42
    • 0034287156 scopus 로고    scopus 로고
    • Asymptotics for Lasso-type estimators
    • Knight K, Fu W. Asymptotics for Lasso-type estimators. Ann Stat 2000;28:1356-78.
    • (2000) Ann Stat , vol.28 , pp. 1356-1378
    • Knight, K.1    Fu, W.2
  • 43
    • 49949095467 scopus 로고    scopus 로고
    • The generalized LASSO: A wrapper approach to gene selection for microarray data. Technical Report IAI-TR-2002-8, University of Bonn, Computer
    • Roth V. The generalized LASSO: A wrapper approach to gene selection for microarray data. Technical Report IAI-TR-2002-8, University of Bonn, Computer Science III, 2002 http://people.inf.ethz.ch/vroth/GenLASSO/ index.html.
    • (2002) Science , vol.3
    • Roth, V.1
  • 44
    • 49949097378 scopus 로고    scopus 로고
    • Incorporating gene functions into regression analysis of DNA-protein binding data and gene expression data to construct transcriptional networks
    • Wei P, Pan W. Incorporating gene functions into regression analysis of DNA-protein binding data and gene expression data to construct transcriptional networks. IEEE Trans Comput Biol Bioinform 2006; 99:1.
    • (2006) IEEE Trans Comput Biol Bioinform , vol.99 , pp. 1
    • Wei, P.1    Pan, W.2
  • 45
    • 0742321914 scopus 로고    scopus 로고
    • Regression approaches for microarray data analysis
    • Segal MR, Dahlquist KD, Conklin BR. Regression approaches for microarray data analysis. J Comput Biol 2003;10:961-80.
    • (2003) J Comput Biol , vol.10 , pp. 961-980
    • Segal, M.R.1    Dahlquist, K.D.2    Conklin, B.R.3
  • 46
    • 33846193774 scopus 로고    scopus 로고
    • A note on the LASSO and related procedures in model selection
    • Leng C, Lin Y, Wahba G. A note on the LASSO and related procedures in model selection. Star Sin 2006;16: 1273-84.
    • (2006) Star Sin , vol.16 , pp. 1273-1284
    • Leng, C.1    Lin, Y.2    Wahba, G.3
  • 47
    • 33845263263 scopus 로고    scopus 로고
    • On model selection consistency of LASSO
    • Zhao P, Yu B. On model selection consistency of LASSO. J Mach Learn Res 2006;7:2541-63.
    • (2006) J Mach Learn Res , vol.7 , pp. 2541-2563
    • Zhao, P.1    Yu, B.2
  • 48
    • 33846114377 scopus 로고    scopus 로고
    • The adaptive Lasso and its oracle properties
    • Zou H. The adaptive Lasso and its oracle properties. JASA 2006; 101:1418-29.
    • (2006) JASA , vol.101 , pp. 1418-1429
    • Zou, H.1
  • 49
    • 49949097774 scopus 로고    scopus 로고
    • Adaptive Lasso for sparse high dimensional regression models
    • In press
    • Huang J, Ma S, Zhang C. Adaptive Lasso for sparse high dimensional regression models. Stat Sin 2007. In press.
    • (2007) Stat Sin
    • Huang, J.1    Ma, S.2    Zhang, C.3
  • 50
    • 33750022956 scopus 로고    scopus 로고
    • Semi-supervised learning via penalized mixture model with application to microarray sample classification
    • Pan W, Shen X, Jiang A, et al. Semi-supervised learning via penalized mixture model with application to microarray sample classification. Bioinformatics 2006;22:2388-95.
    • (2006) Bioinformatics , vol.22 , pp. 2388-2395
    • Pan, W.1    Shen, X.2    Jiang, A.3
  • 51
    • 84952149204 scopus 로고
    • A statistical view of some chemometrics regression tools (with discussion)
    • Frank IE, Friedman JH. A statistical view of some chemometrics regression tools (with discussion). Technometrics 1993;35 109-48.
    • (1993) Technometrics , vol.35 , pp. 109-148
    • Frank, I.E.1    Friedman, J.H.2
  • 52
    • 0032361278 scopus 로고    scopus 로고
    • Penalized regressions: The bridge versus the Lasso
    • Fu W. Penalized regressions: The bridge versus the Lasso. J Comput Graph Stat 1998;7:397-416.
    • (1998) J Comput Graph Stat , vol.7 , pp. 397-416
    • Fu, W.1
  • 53
    • 49949115667 scopus 로고    scopus 로고
    • Asymptotic properties of bridge estimators in sparse high-dimensional regression models
    • Huang J, Horowitz J, Ma S. Asymptotic properties of bridge estimators in sparse high-dimensional regression models. Ann Stat 2008;36 587-613.
    • (2008) Ann Stat , vol.36 , pp. 587-613
    • Huang, J.1    Horowitz, J.2    Ma, S.3
  • 54
    • 16244401458 scopus 로고    scopus 로고
    • Regularization and variable selection via the elastic net
    • Zou H, Hastie T. Regularization and variable selection via the elastic net. J Roy Stat Soc B 2005;67:301-20.
    • (2005) J Roy Stat Soc B , vol.67 , pp. 301-320
    • Zou, H.1    Hastie, T.2
  • 55
    • 1542784498 scopus 로고    scopus 로고
    • Variable selection via nonconcave penalized likelihood and its oracle properties
    • Fan J, Li R. Variable selection via nonconcave penalized likelihood and its oracle properties. J Am Stat Assoc 2001;96: 1348-60.
    • (2001) J Am Stat Assoc , vol.96 , pp. 1348-1360
    • Fan, J.1    Li, R.2
  • 56
    • 34547840186 scopus 로고    scopus 로고
    • Group SCAD regression analysis for microarray time course gene expression data
    • Wang L, Chen G, Li H. Group SCAD regression analysis for microarray time course gene expression data. Bioinformatics 2007;23:1486-94.
    • (2007) Bioinformatics , vol.23 , pp. 1486-1494
    • Wang, L.1    Chen, G.2    Li, H.3
  • 58
    • 3242708140 scopus 로고    scopus 로고
    • Least angle regression (with discussion)
    • Efron B, Hastie T, Johnstone I, et al. Least angle regression (with discussion). Ann Stat 2004;32:407-99.
    • (2004) Ann Stat , vol.32 , pp. 407-499
    • Efron, B.1    Hastie, T.2    Johnstone, I.3
  • 59
    • 34547849507 scopus 로고    scopus 로고
    • 1 regularization path algorithm for generalized linear models
    • 1 regularization path algorithm for generalized linear models. J Roy Stat Soc B 2007;69:659-77.
    • (2007) J Roy Stat Soc B , vol.69 , pp. 659-677
    • Park, M.Y.1    Hastie, T.2
  • 61
    • 34347398269 scopus 로고    scopus 로고
    • Additive risk survival model with microarray data
    • Ma S, Huang J. Additive risk survival model with microarray data. BMC Bioinformatics 2007;8:192.
    • (2007) BMC Bioinformatics , vol.8 , pp. 192
    • Ma, S.1    Huang, J.2
  • 62
    • 18244409687 scopus 로고    scopus 로고
    • Gene expression profiling predicts clinical outcome of breast cancer
    • van't Veer LJ, Dai H, van de Vijver MJ, et al. Gene expression profiling predicts clinical outcome of breast cancer. Nature 2002;415:530-6.
    • (2002) Nature , vol.415 , pp. 530-536
    • van't Veer, L.J.1    Dai, H.2    van de Vijver, M.J.3
  • 63
    • 33846572884 scopus 로고    scopus 로고
    • Empirical study of supervised gene screening
    • Ma S. Empirical study of supervised gene screening. BMC Bioinformatics 2006;7:537.
    • (2006) BMC Bioinformatics , vol.7 , pp. 537
    • Ma, S.1


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.