메뉴 건너뛰기




Volumn 88, Issue , 2017, Pages 45-57

Metabolic pathway synthesis based on predicting compound transformable pairs by using neural classifiers with imbalanced data handling

Author keywords

Classification; Feature; Imbalanced data; Metabolite; Neural networks; Pathway

Indexed keywords

BIOMOLECULES; COMPUTER SYSTEM RECOVERY; DATA HANDLING; ESCHERICHIA COLI; FORECASTING; METABOLISM; METABOLITES; NEURAL NETWORKS; PRINCIPAL COMPONENT ANALYSIS; RECOVERY;

EID: 85021632147     PISSN: 09574174     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.eswa.2017.06.026     Document Type: Article
Times cited : (5)

References (59)
  • 4
    • 27144531570 scopus 로고    scopus 로고
    • A study of the behavior of several methods for balancing machine learning training data
    • Batista, G.E.A.P.A., Prati, R.C., Monard, M.C., A study of the behavior of several methods for balancing machine learning training data. SIGKDD Explorations Newsletter 6:1 (2004), 20–29, 10.1145/1007730.1007735.
    • (2004) SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 20-29
    • Batista, G.E.A.P.A.1    Prati, R.C.2    Monard, M.C.3
  • 5
    • 84921689324 scopus 로고    scopus 로고
    • Classifying imbalanced data sets using similarity based hierarchical decomposition
    • Beyan, C., Fisher, R., Classifying imbalanced data sets using similarity based hierarchical decomposition. Pattern Recognition 48:5 (2015), 1653–1672, 10.1016/j.patcog.2014.10.032.
    • (2015) Pattern Recognition , vol.48 , Issue.5 , pp. 1653-1672
    • Beyan, C.1    Fisher, R.2
  • 6
    • 77957988489 scopus 로고    scopus 로고
    • Class prediction for high-dimensional class-imbalanced data
    • Blagus, R., Lusa, L., Class prediction for high-dimensional class-imbalanced data. BMC Bioinformatics 11:1 (2010), 1–17, 10.1186/1471-2105-11-523.
    • (2010) BMC Bioinformatics , vol.11 , Issue.1 , pp. 1-17
    • Blagus, R.1    Lusa, L.2
  • 7
    • 1542576024 scopus 로고    scopus 로고
    • Biological data mining with neural networks: implementation and application of a flexible decision tree extraction algorithm to genomic problem domains
    • Browne, A., Hudson, B.D., Whitley, D.C., Ford, M.G., Picton, P., Biological data mining with neural networks: implementation and application of a flexible decision tree extraction algorithm to genomic problem domains. Neurocomputing 57 (2004), 275–293, 10.1016/j.neucom.2003.10.007.
    • (2004) Neurocomputing , vol.57 , pp. 275-293
    • Browne, A.1    Hudson, B.D.2    Whitley, D.C.3    Ford, M.G.4    Picton, P.5
  • 9
    • 84862140885 scopus 로고    scopus 로고
    • DBSMOTE: Density-based synthetic minority over-sampling technique
    • Bunkhumpornpat, C., Sinapiromsaran, K., Lursinsap, C., DBSMOTE: Density-based synthetic minority over-sampling technique. Applied Intelligence 36:3 (2012), 664–684, 10.1007/s10489-011-0287-y.
    • (2012) Applied Intelligence , vol.36 , Issue.3 , pp. 664-684
    • Bunkhumpornpat, C.1    Sinapiromsaran, K.2    Lursinsap, C.3
  • 10
    • 37949004300 scopus 로고    scopus 로고
    • Data mining for imbalanced datasets: An overview
    • O. Maimon L. Rokach Springer US
    • Chawla, N., Data mining for imbalanced datasets: An overview. Maimon, O., Rokach, L., (eds.) Data mining and knowledge discovery handbook, 2005, Springer US, 853–867, 10.1007/0-387-25465-X_40.
    • (2005) Data mining and knowledge discovery handbook , pp. 853-867
    • Chawla, N.1
  • 12
    • 77957793322 scopus 로고    scopus 로고
    • RAMOBoost: Ranked minority oversampling in boosting
    • Chen, S., He, H., Garcia, E., RAMOBoost: Ranked minority oversampling in boosting. IEEE Transactions on Neural Networks 21:10 (2010), 1624–1642, 10.1109/TNN.2010.2066988.
    • (2010) IEEE Transactions on Neural Networks , vol.21 , Issue.10 , pp. 1624-1642
    • Chen, S.1    He, H.2    Garcia, E.3
  • 13
    • 37549029793 scopus 로고    scopus 로고
    • The properties of high-dimensional data spaces: implications for exploring gene and protein expression data
    • Clarke, R., Ressom, H.W., Wang, A., Xuan, J., Liu, M.C., Gehan, E.A., Wang, Y., The properties of high-dimensional data spaces: implications for exploring gene and protein expression data. Nature Reviews Cancer 8:1 (2008), 37–49, 10.1038/nrc2294.
    • (2008) Nature Reviews Cancer , vol.8 , Issue.1 , pp. 37-49
    • Clarke, R.1    Ressom, H.W.2    Wang, A.3    Xuan, J.4    Liu, M.C.5    Gehan, E.A.6    Wang, Y.7
  • 14
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes, C., Vapnik, V., Support-vector networks. Machine Learning 20:3 (1995), 273–297, 10.1007/BF00994018.
    • (1995) Machine Learning , vol.20 , Issue.3 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 15
    • 22844434823 scopus 로고    scopus 로고
    • Classification methodologies of multilayer perceptrons with sigmoid activation functions
    • Daqi, G., Yan, J., Classification methodologies of multilayer perceptrons with sigmoid activation functions. Pattern Recognition 38:10 (2005), 1469–1482, 10.1016/j.patcog.2005.03.024.
    • (2005) Pattern Recognition , vol.38 , Issue.10 , pp. 1469-1482
    • Daqi, G.1    Yan, J.2
  • 16
    • 29644438050 scopus 로고    scopus 로고
    • Statistical comparisons of classifiers over multiple data sets
    • Demšar, J., Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research 7 (2006), 1–30.
    • (2006) Journal of Machine Learning Research , vol.7 , pp. 1-30
    • Demšar, J.1
  • 17
    • 57549102595 scopus 로고    scopus 로고
    • Genome-scale models of bacterial metabolism: reconstruction and applications
    • Durot, M., Bourguignon, P.-Y., Schachter, V., Genome-scale models of bacterial metabolism: reconstruction and applications. FEMS Microbiology Reviews 33:1 (2009), 164–190, 10.1111/j.1574-6976.2008.00146.x.
    • (2009) FEMS Microbiology Reviews , vol.33 , Issue.1 , pp. 164-190
    • Durot, M.1    Bourguignon, P.-Y.2    Schachter, V.3
  • 21
    • 84881072864 scopus 로고    scopus 로고
    • Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling
    • Galar, M., Fernández, A., Barrenechea, E., Herrera, F., Eusboost: Enhancing ensembles for highly imbalanced data-sets by evolutionary undersampling. Pattern Recognition 46:12 (2013), 3460–3471, 10.1016/j.patcog.2013.05.006.
    • (2013) Pattern Recognition , vol.46 , Issue.12 , pp. 3460-3471
    • Galar, M.1    Fernández, A.2    Barrenechea, E.3    Herrera, F.4
  • 22
    • 0036081122 scopus 로고    scopus 로고
    • LIGAND: Database of chemical compounds and reactions in biological pathways
    • Goto, S., Okuno, Y., Hattori, M., Nishioka, T., Kanehisa, M., LIGAND: Database of chemical compounds and reactions in biological pathways. Nucleic Acids Research 30:1 (2002), 402–404, 10.1093/nar/30.1.402.
    • (2002) Nucleic Acids Research , vol.30 , Issue.1 , pp. 402-404
    • Goto, S.1    Okuno, Y.2    Hattori, M.3    Nishioka, T.4    Kanehisa, M.5
  • 27
    • 84990941766 scopus 로고    scopus 로고
    • Data mining: Concepts and techniques
    • 3rd Morgan Kaufmann Publishers Inc San Francisco, CA, USA
    • Han, J., Kamber, M., Pei, J., Data mining: Concepts and techniques. 3rd, 2011, Morgan Kaufmann Publishers Inc, San Francisco, CA, USA.
    • (2011)
    • Han, J.1    Kamber, M.2    Pei, J.3
  • 28
    • 77954288984 scopus 로고    scopus 로고
    • SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses
    • Hattori, M., Tanaka, N., Kanehisa, M., Goto, S., SIMCOMP/SUBCOMP: Chemical structure search servers for network analyses. Nucleic Acids Research 38:suppl 2 (2010), W652–W656, 10.1093/nar/gkq367.
    • (2010) Nucleic Acids Research , vol.38 , pp. W652-W656
    • Hattori, M.1    Tanaka, N.2    Kanehisa, M.3    Goto, S.4
  • 30
    • 77954188312 scopus 로고    scopus 로고
    • Finding metabolic pathways using atom tracking
    • Heath, A.P., Bennett, G.N., Kavraki, L.E., Finding metabolic pathways using atom tracking. Bioinformatics 26:12 (2010), 1548–1555, 10.1093/bioinformatics/btq223.
    • (2010) Bioinformatics , vol.26 , Issue.12 , pp. 1548-1555
    • Heath, A.P.1    Bennett, G.N.2    Kavraki, L.E.3
  • 31
    • 78651301358 scopus 로고    scopus 로고
    • Computing atom mappings for biochemical reactions without subgraph isomorphism
    • Heinonen, M., Lappalainen, S., Mielikäinen, T., Rousu, J., Computing atom mappings for biochemical reactions without subgraph isomorphism. Journal of Computational Biology 18:1 (2011), 43–58, 10.1089/cmb.2009.0216.
    • (2011) Journal of Computational Biology , vol.18 , Issue.1 , pp. 43-58
    • Heinonen, M.1    Lappalainen, S.2    Mielikäinen, T.3    Rousu, J.4
  • 33
    • 79952441195 scopus 로고    scopus 로고
    • A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function
    • Hwang, J.P., Park, S., Kim, E., A new weighted approach to imbalanced data classification problem via support vector machine with quadratic cost function. Expert Systems with Applications 38:7 (2011), 8580–8585, 10.1016/j.eswa.2011.01.061.
    • (2011) Expert Systems with Applications , vol.38 , Issue.7 , pp. 8580-8585
    • Hwang, J.P.1    Park, S.2    Kim, E.3
  • 34
    • 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, 7(8), 2012, e41882, 10.1371/journal.pone.0041882.
    • (2012) PLoS One , vol.7 , Issue.8 , pp. e41882
    • Jurman, G.1    Riccadonna, S.2    Furlanello, C.3
  • 35
    • 0033982936 scopus 로고    scopus 로고
    • KEGG: Kyoto encyclopedia of genes and genomes
    • Kanehisa, M., Goto, S., KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Research 28:1 (2000), 27–30, 10.1093/nar/28.1.27.
    • (2000) Nucleic Acids Research , vol.28 , Issue.1 , pp. 27-30
    • Kanehisa, M.1    Goto, S.2
  • 37
    • 84902497562 scopus 로고    scopus 로고
    • KCF-S: KEGG chemical function and substructure for improved interpretability and prediction in chemical bioinformatics
    • Kotera, M., Tabei, Y., Yamanishi, Y., Moriya, Y., Tokimatsu, T., Kanehisa, M., Goto, S., KCF-S: KEGG chemical function and substructure for improved interpretability and prediction in chemical bioinformatics. BMC Systems Biology 7:6 (2013), 1–17, 10.1186/1752-0509-7-S6-S2.
    • (2013) BMC Systems Biology , vol.7 , Issue.6 , pp. 1-17
    • Kotera, M.1    Tabei, Y.2    Yamanishi, Y.3    Moriya, Y.4    Tokimatsu, T.5    Kanehisa, M.6    Goto, S.7
  • 38
    • 84902435876 scopus 로고    scopus 로고
    • Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach
    • Kotera, M., Tabei, Y., Yamanishi, Y., Muto, A., Moriya, Y., Tokimatsu, T., Goto, S., Metabolome-scale prediction of intermediate compounds in multistep metabolic pathways with a recursive supervised approach. Bioinformatics 30:12 (2014), i165–i174, 10.1093/bioinformatics/btu265.
    • (2014) Bioinformatics , vol.30 , Issue.12 , pp. i165-i174
    • Kotera, M.1    Tabei, Y.2    Yamanishi, Y.3    Muto, A.4    Moriya, Y.5    Tokimatsu, T.6    Goto, S.7
  • 39
    • 84879916305 scopus 로고    scopus 로고
    • Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets
    • Kotera, M., Tabei, Y., Yamanishi, Y., Tokimatsu, T., Goto, S., Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets. Bioinformatics 29:13 (2013), i135–i144, 10.1093/bioinformatics/btt244.
    • (2013) Bioinformatics , vol.29 , Issue.13 , pp. i135-i144
    • Kotera, M.1    Tabei, Y.2    Yamanishi, Y.3    Tokimatsu, T.4    Goto, S.5
  • 40
    • 85021653922 scopus 로고    scopus 로고
    • Network analysis tools (NeAT): Metabolic pathfinder. Accessed 05.08.10.
    • Laboratoire de Bioinformatique des Génomes et des Réseaux (BiGRe) (2010). Network analysis tools (NeAT): Metabolic pathfinder. http://rsat.bigre.ulb.ac.be/pathfindingsupplementref/ReferencePathways.html. Accessed 05.08.10.
    • (2010)
  • 41
    • 0345863903 scopus 로고    scopus 로고
    • The amaze lightbench: A web interface to a relational database of cellular processes.
    • Lemer, C., Antezana, E., Couche, F., Fays, F., Santolaria, X., Janky, R., Wodak, S.J., The amaze lightbench: A web interface to a relational database of cellular processes. Nucleic Acids Research 32:Database-Issue (2004), 443–448, 10.1093/nar/gkh139.
    • (2004) Nucleic Acids Research , vol.32 , Issue.Database-Issue , pp. 443-448
    • Lemer, C.1    Antezana, E.2    Couche, F.3    Fays, F.4    Santolaria, X.5    Janky, R.6    Wodak, S.J.7
  • 44
    • 79957842729 scopus 로고    scopus 로고
    • Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds
    • Mu, F., Unkefer, C.J., Unkefer, P.J., Hlavacek, W.S., Prediction of metabolic reactions based on atomic and molecular properties of small-molecule compounds. Bioinformatics 27:11 (2011), 1537–1545, 10.1093/bioinformatics/btr177.
    • (2011) Bioinformatics , vol.27 , Issue.11 , pp. 1537-1545
    • Mu, F.1    Unkefer, C.J.2    Unkefer, P.J.3    Hlavacek, W.S.4
  • 45
    • 0004026407 scopus 로고    scopus 로고
    • Lehninger principles of biochemistry
    • 4th W. H. Freeman New York
    • Nelson, D.L., Cox, M.M., Lehninger principles of biochemistry. 4th, 2004, W. H. Freeman, New York.
    • (2004)
    • Nelson, D.L.1    Cox, M.M.2
  • 46
    • 0003500248 scopus 로고
    • C4.5: Programs for machine learning
    • Morgan Kaufmann Publishers Inc. San Francisco, CA, USA
    • Quinlan, J.R., C4.5: Programs for machine learning. 1993, Morgan Kaufmann Publishers Inc., San Francisco, CA, USA.
    • (1993)
    • Quinlan, J.R.1
  • 47
    • 0036522746 scopus 로고    scopus 로고
    • Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm
    • Raymond, J.W., Gardiner, E.J., Willett, P., Heuristics for similarity searching of chemical graphs using a maximum common edge subgraph algorithm. Journal of Chemical Information and Computer Sciences 42:2 (2002), 305–316, 10.1021/ci010381f.
    • (2002) Journal of Chemical Information and Computer Sciences , vol.42 , Issue.2 , pp. 305-316
    • Raymond, J.W.1    Gardiner, E.J.2    Willett, P.3
  • 48
    • 0023453329 scopus 로고
    • Silhouettes: A graphical aid to the interpretation and validation of cluster analysis
    • Rousseeuw, P.J., Silhouettes: A graphical aid to the interpretation and validation of cluster analysis. Journal of Applied Mathematics and Computing 20 (1987), 53–65, 10.1016/0377-0427(87)90125-7.
    • (1987) Journal of Applied Mathematics and Computing , vol.20 , pp. 53-65
    • Rousseeuw, P.J.1
  • 49
    • 35748932917 scopus 로고    scopus 로고
    • A review of feature selection techniques in bioinformatics
    • Saeys, Y., Inza, I.n., Larrañaga, P., A review of feature selection techniques in bioinformatics. Bioinformatics 23:19 (2007), 2507–2517, 10.1093/bioinformatics/btm344.
    • (2007) Bioinformatics , vol.23 , Issue.19 , pp. 2507-2517
    • Saeys, Y.1    Inza, I.N.2    Larrañaga, P.3
  • 51
    • 35448937584 scopus 로고    scopus 로고
    • Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements
    • Stewart, J.J.P., Optimization of parameters for semiempirical methods V: Modification of NDDO approximations and application to 70 elements. Journal of Molecular Model 13:12 (2007), 1173–1213, 10.1007/s00894-007-0233-4.
    • (2007) Journal of Molecular Model , vol.13 , Issue.12 , pp. 1173-1213
    • Stewart, J.J.P.1
  • 53
    • 84878457382 scopus 로고    scopus 로고
    • Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and adaboost techniques
    • Thanathamathee, P., Lursinsap, C., Handling imbalanced data sets with synthetic boundary data generation using bootstrap re-sampling and adaboost techniques. Pattern Recognition Letters 34:12 (2013), 1339–1347, 10.1016/j.patrec.2013.04.019.
    • (2013) Pattern Recognition Letters , vol.34 , Issue.12 , pp. 1339-1347
    • Thanathamathee, P.1    Lursinsap, C.2
  • 55
    • 77956023732 scopus 로고    scopus 로고
    • Combating the small sample class imbalance problem using feature selection
    • Wasikowski, M., w. Chen, X., Combating the small sample class imbalance problem using feature selection. IEEE Transactions on Knowledge and Data Engineering 22:10 (2010), 1388–1400, 10.1109/TKDE.2009.187.
    • (2010) IEEE Transactions on Knowledge and Data Engineering , vol.22 , Issue.10 , pp. 1388-1400
    • Wasikowski, M.1    w. Chen, X.2
  • 56
    • 45949104347 scopus 로고    scopus 로고
    • Recent applications of neural networks in bioinformatics
    • B. Apolloni M. Marinaro R. Tagliaferri Springer Netherlands
    • Wood, M.J., Hirst, J.D., Recent applications of neural networks in bioinformatics. Apolloni, B., Marinaro, M., Tagliaferri, R., (eds.) Biological and artificial intelligence environments, 2005, Springer Netherlands, 91–97, 10.1007/1-4020-3432-6_11.
    • (2005) Biological and artificial intelligence environments , pp. 91-97
    • Wood, M.J.1    Hirst, J.D.2
  • 57
    • 33846986488 scopus 로고    scopus 로고
    • An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics
    • Yoon, K., Kwek, S., An unsupervised learning approach to resolving the data imbalanced issue in supervised learning problems in functional genomics. Fifth international conference on hybrid intelligent systems (HIS'05), 2005, 6, 10.1109/ICHIS.2005.23.
    • (2005) Fifth international conference on hybrid intelligent systems (HIS'05) , pp. 6
    • Yoon, K.1    Kwek, S.2
  • 58
    • 16644402628 scopus 로고    scopus 로고
    • Feature selection for text categorization on imbalanced data
    • Zheng, Z., Wu, X., Srihari, R., Feature selection for text categorization on imbalanced data. SIGKDD Explorations Newsletter 6:1 (2004), 80–89, 10.1145/1007730.1007741.
    • (2004) SIGKDD Explorations Newsletter , vol.6 , Issue.1 , pp. 80-89
    • Zheng, Z.1    Wu, X.2    Srihari, R.3
  • 59
    • 79960138128 scopus 로고    scopus 로고
    • The strength of chemical linkage as a criterion for pruning metabolic graphs
    • Zhou, W., Nakhleh, L., The strength of chemical linkage as a criterion for pruning metabolic graphs. Bioinformatics 27:14 (2011), 1957–1963, 10.1093/bioinformatics/btr271.
    • (2011) Bioinformatics , vol.27 , Issue.14 , pp. 1957-1963
    • Zhou, W.1    Nakhleh, L.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.