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Volumn 1, Issue 1, 2011, Pages 55-63

The use of classification trees for bioinformatics

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; LEARNING SYSTEMS;

EID: 84857095805     PISSN: 19424787     EISSN: 19424795     Source Type: Journal    
DOI: 10.1002/widm.14     Document Type: Article
Times cited : (86)

References (58)
  • 1
    • 0029365976 scopus 로고
    • Locating protein coding regions in human DNA using a decision tree algorithm
    • Salzberg S. Locating protein coding regions in human DNA using a decision tree algorithm. J Comput Biol 1995, 2:473-485.
    • (1995) J Comput Biol , vol.2 , pp. 473-485
    • Salzberg, S.1
  • 3
    • 84873126382 scopus 로고    scopus 로고
    • Ensemble learning algorithms for classification of mtDNA into haplogroups
    • Epub ahead of print;March 4
    • Wong C, Li Y, Lee C, Huang CH. Ensemble learning algorithms for classification of mtDNA into haplogroups. Brief Bioinform (Epub ahead of print;March 4, 2010).
    • (2010) Brief Bioinform
    • Wong, C.1    Li, Y.2    Lee, C.3    Huang, C.H.4
  • 6
    • 6944251719 scopus 로고    scopus 로고
    • Predicting gene function in Saccharomyces cerevisiae
    • Clare A, King RD. Predicting gene function in Saccharomyces cerevisiae. Bioinformatics 2003, 19(suppl 2):ii42-49.
    • (2003) Bioinformatics , vol.19 , Issue.SUPPL. 2
    • Clare, A.1    King, R.D.2
  • 7
    • 0038681269 scopus 로고    scopus 로고
    • Statistical models for discerning protein structures containing the DNAbinding helix-turn-helix motif
    • McLaughlin WA, Berman HM. Statistical models for discerning protein structures containing the DNAbinding helix-turn-helix motif. J Mol Biol 2003, 330:43-55.
    • (2003) J Mol Biol , vol.330 , pp. 43-55
    • McLaughlin, W.A.1    Berman, H.M.2
  • 8
    • 37849032668 scopus 로고    scopus 로고
    • 'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools
    • Shen YQ, Burger G. 'Unite and conquer': enhanced prediction of protein subcellular localization by integrating multiple specialized tools. BMC Bioinformatics 2007, 8:420.
    • (2007) BMC Bioinformatics , vol.8 , pp. 420
    • Shen, Y.Q.1    Burger, G.2
  • 9
    • 77149177676 scopus 로고    scopus 로고
    • Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics
    • Jung J, Ryu T, Hwang Y, Lee E, Lee D. Prediction of extracellular matrix proteins based on distinctive sequence and domain characteristics. J Comput Biol 2010, 17:97-105.
    • (2010) J Comput Biol , vol.17 , pp. 97-105
    • Jung, J.1    Ryu, T.2    Hwang, Y.3    Lee, E.4    Lee, D.5
  • 10
    • 13244265581 scopus 로고    scopus 로고
    • Information assessment on predicting protein-protein interactions
    • Lin N, Wu B, Jansen R, GersteinM, Zhao H. Information assessment on predicting protein-protein interactions. BMC Bioinformatics 2004, 5:154.
    • (2004) BMC Bioinformatics , vol.5 , pp. 154
    • Lin, N.1    Wu, B.2    Jansen, R.3    Gerstein, M.4    Zhao, H.5
  • 11
    • 75149162532 scopus 로고    scopus 로고
    • Active learning for human protein-protein interaction prediction
    • Mohamed TP, Carbonell JG, Ganapathiraju MK. Active learning for human protein-protein interaction prediction. BMC Bioinformatics 2010, 11(suppl 1): S57.
    • (2010) BMC Bioinformatics , vol.11 , Issue.SUPPL. 1
    • Mohamed, T.P.1    Carbonell, J.G.2    Ganapathiraju, M.K.3
  • 12
    • 0029036696 scopus 로고
    • Analysis of a 2(9) full factorial chemical library
    • Young SS, Hawkins DM. Analysis of a 2(9) full factorial chemical library. J Med Chem 1995, 38:2784-2788.
    • (1995) J Med Chem , vol.38 , pp. 2784-2788
    • Young, S.S.1    Hawkins, D.M.2
  • 14
    • 0004255908 scopus 로고    scopus 로고
    • New York:McGraw Hill Higher Education
    • Mitchell TM. Machine Learning. New York:McGraw Hill Higher Education; 1997.
    • (1997) Machine Learning
    • Mitchell, T.M.1
  • 15
    • 33644752875 scopus 로고    scopus 로고
    • Decision trees for classification: a review and some new results
    • Pal SK, Pla A, eds. Singapore: World Scientific Publishing Company
    • Kothari R, Dong M. Decision trees for classification: a review and some new results. In: Pal SK, Pla A, eds. Pattern Recognition from Classical to Modern Approaches. Singapore: World Scientific Publishing Company; 2002 169-186.
    • (2002) Pattern Recognition from Classical to Modern Approaches , pp. 169-186
    • Kothari, R.1    Dong, M.2
  • 16
    • 70649114623 scopus 로고    scopus 로고
    • Navigating random forests and related advances in algorithmic modeling
    • Siroky DS. Navigating random forests and related advances in algorithmic modeling. Stat Surv 2009, 3:147-163.
    • (2009) Stat Surv , vol.3 , pp. 147-163
    • Siroky, D.S.1
  • 17
    • 33644855553 scopus 로고    scopus 로고
    • Decision tree methods in pharmaceutical research
    • Blower PE, Cross KP. Decision tree methods in pharmaceutical research. Curr TopMed Chem 2006, 6:31-39.
    • (2006) Curr TopMed Chem , vol.6 , pp. 31-39
    • Blower, P.E.1    Cross, K.P.2
  • 19
    • 33744584654 scopus 로고
    • Induction of decision trees
    • Quinlan JR. Induction of decision trees. Mach Learn 1986, 1:81-106.
    • (1986) Mach Learn , vol.1 , pp. 81-106
    • Quinlan, J.R.1
  • 22
    • 0042942219 scopus 로고    scopus 로고
    • Families of splitting criteria for classification trees
    • Shih Y-S. Families of splitting criteria for classification trees. Stat Comput 1999, 9:309-315.
    • (1999) Stat Comput , vol.9 , pp. 309-315
    • Shih, Y.-S.1
  • 23
    • 0042409675 scopus 로고    scopus 로고
    • Selecting the best categorical split for classification trees
    • Shih Y-S. Selecting the best categorical split for classification trees. Stat Probab Lett 2001, 54:341-345.
    • (2001) Stat Probab Lett , vol.54 , pp. 341-345
    • Shih, Y.-S.1
  • 24
    • 65849418129 scopus 로고    scopus 로고
    • A tree-based method for modeling a multivariate ordinal response
    • Zhang H, Ye Y. A tree-based method for modeling a multivariate ordinal response. Stat Interface 2008, 1:169-178.
    • (2008) Stat Interface , vol.1 , pp. 169-178
    • Zhang, H.1    Ye, Y.2
  • 26
    • 33846643930 scopus 로고    scopus 로고
    • Exploiting interactions among polymorphisms contributing to complex disease traitswith boosted generative modeling
    • Wang LY, Comaniciu D, Fasulo D. Exploiting interactions among polymorphisms contributing to complex disease traitswith boosted generative modeling. J Comput Biol 2006, 13:1673-1684.
    • (2006) J Comput Biol , vol.13 , pp. 1673-1684
    • Wang, L.Y.1    Comaniciu, D.2    Fasulo, D.3
  • 28
    • 34547596338 scopus 로고    scopus 로고
    • MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features
    • Jiang P, Wu H, WangW, MaW, Sun X, Lu Z. MiPred: classification of real and pseudo microRNA precursors using random forest prediction model with combined features. Nucleic Acids Res 2007, 35:W339-344.
    • (2007) Nucleic Acids Res , vol.35
    • Jiang, P.1    Wu, H.2    Wang, W.3    Ma, W.4    Sun, X.5    Lu, Z.6
  • 29
    • 0035478854 scopus 로고    scopus 로고
    • Random Forests
    • Breiman L. Random Forests. Mach Learn 2001, 45:5-32.
    • (2001) Mach Learn , vol.45 , pp. 5-32
    • Breiman, L.1
  • 31
    • 37649007447 scopus 로고    scopus 로고
    • A forest-based approach to identifying gene and gene-gene interactions
    • Chen X, Liu CT, Zhang M, Zhang H. A forest-based approach to identifying gene and gene-gene interactions. Proc Natl Acad Sci USA 2007, 104:19199-19203.
    • (2007) Proc Natl Acad Sci USA , vol.104 , pp. 19199-19203
    • Chen, X.1    Liu, C.T.2    Zhang, M.3    Zhang, H.4
  • 32
    • 0037388166 scopus 로고    scopus 로고
    • Cell and tumor classification using gene expression data: construction of forests
    • Zhang H, Yu C-Y, Singer B. Cell and tumor classification using gene expression data: construction of forests. Proc Natl Acad Sci USA 2003, 100:4168-4172.
    • (2003) Proc Natl Acad Sci USA , vol.100 , pp. 4168-4172
    • Zhang, H.1    Yu, C.-Y.2    Singer, B.3
  • 34
    • 33847096395 scopus 로고    scopus 로고
    • Bias in random forest variable importance measures: illustrations, sources and a solution
    • Strobl C, Boulesteix AL, Zeileis A, Hothorn T. Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinformatics 2007, 8:25.
    • (2007) BMC Bioinformatics , vol.8 , pp. 25
    • Strobl, C.1    Boulesteix, A.L.2    Zeileis, A.3    Hothorn, T.4
  • 35
    • 77951970995 scopus 로고    scopus 로고
    • aximal conditional chisquare importance in random forests
    • Wang M, Chen X, Zhang H. Maximal conditional chisquare importance in random forests. Bioinformatics 2010, 26:831-837.
    • (2010) Bioinformatics , vol.26 , pp. 831-837
    • Wang, M.1    Chen, X.2    Zhang, H.3
  • 37
    • 67650770061 scopus 로고    scopus 로고
    • Predictor correlation impacts machine learning algorithms: implications for genomic studies
    • Nicodemus KK, Malley JD. Predictor correlation impacts machine learning algorithms: implications for genomic studies. Bioinformatics 2009, 25:1884-1890.
    • (2009) Bioinformatics , vol.25 , pp. 1884-1890
    • Nicodemus, K.K.1    Malley, J.D.2
  • 39
    • 84863447426 scopus 로고    scopus 로고
    • Search for the smallest random forest
    • Zhang H, Wang M. Search for the smallest random forest. Stat Interface 2009, 2:381.
    • (2009) Stat Interface , vol.2 , pp. 381
    • Zhang, H.1    Wang, M.2
  • 41
    • 0035735265 scopus 로고    scopus 로고
    • Computational identification of promoters and first exons in the human genome
    • Davuluri RV, Grosse I, Zhang MQ. Computational identification of promoters and first exons in the human genome. Nat Genet 2001, 29:412-417.
    • (2001) Nat Genet , vol.29 , pp. 412-417
    • Davuluri, R.V.1    Grosse, I.2    Zhang, M.Q.3
  • 43
    • 41049110433 scopus 로고    scopus 로고
    • Functional discrimination of membrane proteins using machine learning techniques
    • Gromiha MM, Yabuki Y. Functional discrimination of membrane proteins using machine learning techniques. BMC Bioinformatics 2008, 9:135.
    • (2008) BMC Bioinformatics , vol.9 , pp. 135
    • Gromiha, M.M.1    Yabuki, Y.2
  • 44
    • 44449122934 scopus 로고    scopus 로고
    • Investigation of transmembrane proteins using a computational approach
    • Yang JY, YangMQ, Dunker AK, Deng Y, Huang X. Investigation of transmembrane proteins using a computational approach. BMC Genomics 2008, 9(suppl 1): S7.
    • (2008) BMC Genomics , vol.9 , Issue.SUPPL. 1
    • Yang, J.Y.1    Yang, M.Q.2    Dunker, A.K.3    Deng, Y.4    Huang, X.5
  • 45
    • 33646018046 scopus 로고    scopus 로고
    • Evaluation of different biological data and computational classification methods for use in protein interaction prediction
    • Qi Y, Bar-Joseph Z, Klein-Seetharaman J. Evaluation of different biological data and computational classification methods for use in protein interaction prediction. Proteins 2006, 63:490-500.
    • (2006) Proteins , vol.63 , pp. 490-500
    • Qi, Y.1    Bar-Joseph, Z.2    Klein-Seetharaman, J.3
  • 46
    • 2942541277 scopus 로고    scopus 로고
    • Predicting cocomplexed protein pairs using genomic and proteomic data integration
    • Zhang LV, Wong SL, KingOD, Roth FP. Predicting cocomplexed protein pairs using genomic and proteomic data integration. BMC Bioinformatics 2004, 5:38.
    • (2004) BMC Bioinformatics , vol.5 , pp. 38
    • Zhang, L.V.1    Wong, S.L.2    King, O.D.3    Roth, F.P.4
  • 47
    • 28944450149 scopus 로고    scopus 로고
    • Prediction of protein-protein interactions using random decision forest framework
    • Chen XW, Liu M. Prediction of protein-protein interactions using random decision forest framework. Bioinformatics 2005, 21:4394-4400.
    • (2005) Bioinformatics , vol.21 , pp. 4394-4400
    • Chen, X.W.1    Liu, M.2
  • 48
    • 77950200613 scopus 로고    scopus 로고
    • Prediction of the clinical phenotype of Fabry disease based on protein sequential and structural information
    • Saito S, Ohno K, Sese J, Sugawara K, Sakuraba H. Prediction of the clinical phenotype of Fabry disease based on protein sequential and structural information. J Hum Genet 2010.
    • (2010) J Hum Genet
    • Saito, S.1    Ohno, K.2    Sese, J.3    Sugawara, K.4    Sakuraba, H.5
  • 51
    • 75149183709 scopus 로고    scopus 로고
    • Virtual screening of bioassay data
    • Schierz AC. Virtual screening of bioassay data. J Cheminform 2009, 1:21.
    • (2009) J Cheminform , vol.1 , pp. 21
    • Schierz, A.C.1
  • 52
    • 77951955992 scopus 로고    scopus 로고
    • Non-linear classification for on-the-fly fractional mass filtering and targeted precursor fragmentation in mass spectrometry experiments
    • Kirchner M, Timm W, Fong P, Wangemann P, Steen H. Non-linear classification for on-the-fly fractional mass filtering and targeted precursor fragmentation in mass spectrometry experiments. Bioinformatics 2010, 26:791-797.
    • (2010) Bioinformatics , vol.26 , pp. 791-797
    • Kirchner, M.1    Timm, W.2    Fong, P.3    Wangemann, P.4    Steen, H.5
  • 53
    • 77349123492 scopus 로고    scopus 로고
    • Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification
    • Ramirez J, Gorriz JM, Segovia F, Chaves R, Salas-Gonzalez D, López M, Alvarez I, Padilla P. Computer aided diagnosis system for the Alzheimer's disease based on partial least squares and random forest SPECT image classification. Neurosci Lett 2010, 472:99-103.
    • (2010) Neurosci Lett , vol.472 , pp. 99-103
    • Ramirez, J.1    Gorriz, J.M.2    Segovia, F.3    Chaves, R.4    Salas-Gonzalez, D.5    López, M.6    Alvarez, I.7    Padilla, P.8
  • 54
    • 0037270514 scopus 로고    scopus 로고
    • Towards reconstruction of gene networks from expression data by supervised learning
    • Soinov LA, Krestyaninova MA, Brazma A. Towards reconstruction of gene networks from expression data by supervised learning. Genome Biol 2003, 4:R6.
    • (2003) Genome Biol , vol.4
    • Soinov, L.A.1    Krestyaninova, M.A.2    Brazma, A.3
  • 55
    • 25444453244 scopus 로고    scopus 로고
    • Screening large-scale association study data: exploiting interactions using random forests
    • Lunetta KL, Hayward LB, Segal J, Van Eerdewegh P. Screening large-scale association study data: exploiting interactions using random forests. BMC Genet 2004, 5:32.
    • (2004) BMC Genet , vol.5 , pp. 32
    • Lunetta, K.L.1    Hayward, L.B.2    Segal, J.3    Van Eerdewegh, P.4
  • 56
    • 77949388276 scopus 로고    scopus 로고
    • The behaviour of random forest permutation-based variable importance measures under predictor correlation
    • Nicodemus KK, Malley JD, Strobl C, Ziegler A. The behaviour of random forest permutation-based variable importance measures under predictor correlation. BMC Bioinformatics 2010, 11:110.
    • (2010) BMC Bioinformatics , vol.11 , pp. 110
    • Nicodemus, K.K.1    Malley, J.D.2    Strobl, C.3    Ziegler, A.4
  • 57
    • 82355167935 scopus 로고    scopus 로고
    • Detecting genes and gene-gene interactions for age-related macular degeneration with a forest-based approach
    • Wang M, Zhang M, Chen X, Zhang H. Detecting genes and gene-gene interactions for age-related macular degeneration with a forest-based approach. Stat Biopharm Res 2009, 1:424-430.
    • (2009) Stat Biopharm Res , vol.1 , pp. 424-430
    • Wang, M.1    Zhang, M.2    Chen, X.3    Zhang, H.4


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