메뉴 건너뛰기




Volumn 8, Issue JUN, 2017, Pages

More is better: Recent progress in multi-omics data integration methods

Author keywords

Integration; Multi omics; Precision medicine; Prediction; Prognosis; Supervised learning; Unsupervised learning

Indexed keywords

CLINICAL OUTCOME; LEARNING; OVERALL SURVIVAL; PERSONALIZED MEDICINE; PREDICTION; PROGNOSIS; SOFTWARE;

EID: 85021210336     PISSN: None     EISSN: 16648021     Source Type: Journal    
DOI: 10.3389/fgene.2017.00084     Document Type: Short Survey
Times cited : (516)

References (59)
  • 1
    • 78649976005 scopus 로고    scopus 로고
    • An integrated approach to uncover drivers of cancer
    • Akavia, U. D., Litvin, O., Kim, J., Sanchez-Garcia, F., Kotliar, D., Causton, H. C., et al. (2010). An integrated approach to uncover drivers of cancer. Cell 143, 1005-1017. doi: 10.1016/j.cell.2010.11.013
    • (2010) Cell , vol.143 , pp. 1005-1017
    • Akavia, U.D.1    Litvin, O.2    Kim, J.3    Sanchez-Garcia, F.4    Kotliar, D.5    Causton, H.C.6
  • 2
    • 84873859243 scopus 로고    scopus 로고
    • Identifying in-trans process associated genes in breast cancer by integrated analysis of copy number and expression data
    • Aure, M. R., Steinfeld, I., Baumbusch, L. O., Liestøl, K., Lipson, D., Nyberg, S., et al. (2013). Identifying in-trans process associated genes in breast cancer by integrated analysis of copy number and expression data. PLoS ONE 8:e53014. doi: 10.1371/journal.pone.0053014
    • (2013) PLoS ONE , vol.8
    • Aure, M.R.1    Steinfeld, I.2    Baumbusch, L.O.3    Liestøl, K.4    Lipson, D.5    Nyberg, S.6
  • 3
    • 84924370895 scopus 로고    scopus 로고
    • Integrative multi-omics module network inference with Lemon-Tree
    • Bonnet, E., Calzone, L., and Michoel, T. (2015). Integrative multi-omics module network inference with Lemon-Tree. PLoS Comput. Biol. 11:e1003983. doi: 10.1371/journal.pcbi.1003983
    • (2015) PLoS Comput. Biol , vol.11
    • Bonnet, E.1    Calzone, L.2    Michoel, T.3
  • 4
    • 77953936121 scopus 로고    scopus 로고
    • An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer
    • Chari, R., Coe, B. P., Vucic, E. A., Lockwood, W. W., and Lam, W. L. (2010). An integrative multi-dimensional genetic and epigenetic strategy to identify aberrant genes and pathways in cancer. BMC Syst. Biol. 4:67. doi: 10.1186/1752-0509-4-67
    • (2010) BMC Syst. Biol , vol.4 , pp. 67
    • Chari, R.1    Coe, B.P.2    Vucic, E.A.3    Lockwood, W.W.4    Lam, W.L.5
  • 5
    • 84973364594 scopus 로고    scopus 로고
    • Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data
    • Chen, J., and Zhang, S. (2016). Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics 32, 1724-1732. doi: 10.1093/bioinformatics/btw059
    • (2016) Bioinformatics , vol.32 , pp. 1724-1732
    • Chen, J.1    Zhang, S.2
  • 6
    • 84874848060 scopus 로고    scopus 로고
    • Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis
    • Chen, J., Bushman, F. D., Lewis, J. D., Wu, G. D., and Li, H. (2013). Structure-constrained sparse canonical correlation analysis with an application to microbiome data analysis. Biostatistics 14, 244-258. doi: 10.1093/biostatistics/kxs038
    • (2013) Biostatistics , vol.14 , pp. 244-258
    • Chen, J.1    Bushman, F.D.2    Lewis, J.D.3    Wu, G.D.4    Li, H.5
  • 7
    • 79952395270 scopus 로고    scopus 로고
    • Cancer genomics: from discovery science to personalized medicine
    • Chin, L., Andersen, J. N., and Futreal, P. A. (2011). Cancer genomics: from discovery science to personalized medicine. Nat. Med. 17, 297-303. doi: 10.1038/nm.2323
    • (2011) Nat. Med , vol.17 , pp. 297-303
    • Chin, L.1    Andersen, J.N.2    Futreal, P.A.3
  • 8
    • 84884923930 scopus 로고    scopus 로고
    • Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model
    • Cho, D.-Y., and Przytycka, T. M. (2013). Dissecting cancer heterogeneity with a probabilistic genotype-phenotype model. Nucleic Acids Res. 41, 8011-8020. doi: 10.1093/nar/gkt577
    • (2013) Nucleic Acids Res , vol.41 , pp. 8011-8020
    • Cho, D.-Y.1    Przytycka, T.M.2
  • 9
    • 38449101120 scopus 로고    scopus 로고
    • Integration of biological networks and gene expression data using Cytoscape
    • Cline, M. S., Smoot, M., Cerami, E., Kuchinsky, A., Landys, N., Workman, C., et al. (2007). Integration of biological networks and gene expression data using Cytoscape. Nat. Protoc. 2, 2366-2382. doi: 10.1038/nprot.2007.324
    • (2007) Nat. Protoc , vol.2 , pp. 2366-2382
    • Cline, M.S.1    Smoot, M.2    Cerami, E.3    Kuchinsky, A.4    Landys, N.5    Workman, C.6
  • 10
    • 84864043341 scopus 로고    scopus 로고
    • 'Infinite latent feature models and the Indian buffet process,'
    • (NIPS 2005) (Vancouver, BC)
    • Griffiths, T. L., and Ghahramani, Z. (2005). "Infinite latent feature models and the Indian buffet process," in Advances in Neural Information Processing Systems 18 (NIPS 2005) (Vancouver, BC), 475-482
    • (2005) Advances in Neural Information Processing Systems 18 , pp. 475-482
    • Griffiths, T.L.1    Ghahramani, Z.2
  • 11
    • 25444532599 scopus 로고    scopus 로고
    • Communicating prognosis in cancer care: a systematic review of the literature
    • Hagerty, R. G., Butow, P. N., Ellis, P. M., Dimitry, S., and Tattersall, M. H. N. (2005). Communicating prognosis in cancer care: a systematic review of the literature. Ann. Oncol. 16, 1005-1053. doi: 10.1093/annonc/mdi211
    • (2005) Ann. Oncol , vol.16 , pp. 1005-1053
    • Hagerty, R.G.1    Butow, P.N.2    Ellis, P.M.3    Dimitry, S.4    Tattersall, M.H.N.5
  • 12
    • 51049096780 scopus 로고    scopus 로고
    • Kernel methods in machine learning
    • Hofmann, T., Schölkopf, B., and Smola, A. J. (2008). Kernel methods in machine learning. Ann. Stat. 36, 1171-1220. doi: 10.1214/009053607000000677
    • (2008) Ann. Stat , vol.36 , pp. 1171-1220
    • Hofmann, T.1    Schölkopf, B.2    Smola, A.J.3
  • 13
    • 84977537667 scopus 로고    scopus 로고
    • Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis
    • Huang, S., Chong, N., Lewis, N. E., Jia, W., Xie, G., and Garmire, L. X. (2016). Novel personalized pathway-based metabolomics models reveal key metabolic pathways for breast cancer diagnosis. Genome Med. 8:34. doi: 10.1186/s13073-016-0289-9
    • (2016) Genome Med , vol.8 , pp. 34
    • Huang, S.1    Chong, N.2    Lewis, N.E.3    Jia, W.4    Xie, G.5    Garmire, L.X.6
  • 14
    • 84907588668 scopus 로고    scopus 로고
    • A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer
    • Huang, S., Yee, C., Ching, T., Yu, H., and Garmire, L. X. (2014). A novel model to combine clinical and pathway-based transcriptomic information for the prognosis prediction of breast cancer. PLoS Comput. Biol. 10:e1003851. doi: 10.1371/journal.pcbi.1003851
    • (2014) PLoS Comput. Biol , vol.10
    • Huang, S.1    Yee, C.2    Ching, T.3    Yu, H.4    Garmire, L.X.5
  • 16
    • 0000801240 scopus 로고    scopus 로고
    • Discovering regulatory and signalling circuits in molecular interaction networks
    • Ideker, T., Ozier, O., Schwikowski, B., and Siegel, A. F. (2002). Discovering regulatory and signalling circuits in molecular interaction networks. Bioinformatics 18, S233-S240. doi: 10.1093/bioinformatics/18.suppl_1.S233
    • (2002) Bioinformatics , vol.18 , pp. S233-S240
    • Ideker, T.1    Ozier, O.2    Schwikowski, B.3    Siegel, A.F.4
  • 17
    • 3242875300 scopus 로고    scopus 로고
    • Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks
    • Imoto, S., Higuchi, T., Goto, T., Tashiro, K., Kuhara, S., and Miyano, S. (2004). Combining microarrays and biological knowledge for estimating gene networks via Bayesian networks. J. Bioinform. Comput. Biol. 2, 77-98. doi: 10.1142/S021972000400048X
    • (2004) J. Bioinform. Comput. Biol , vol.2 , pp. 77-98
    • Imoto, S.1    Higuchi, T.2    Goto, T.3    Tashiro, K.4    Kuhara, S.5    Miyano, S.6
  • 19
    • 85021214487 scopus 로고    scopus 로고
    • Data integration for cancer clinical outcome prediction
    • Kim, D., and Ritchie, M. D. (2014). Data integration for cancer clinical outcome prediction. J. Heal. Med. Informatics 5:e122. doi: 10.4172/2157-7420.1000e122
    • (2014) J. Heal. Med. Informatics , vol.5
    • Kim, D.1    Ritchie, M.D.2
  • 20
    • 84890485602 scopus 로고    scopus 로고
    • ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network
    • Kim, D., Li, R., Dudek, S. M., and Ritchie, M. D. (2013). ATHENA: Identifying interactions between different levels of genomic data associated with cancer clinical outcomes using grammatical evolution neural network. BioData Min. 6:23. doi: 10.1186/1756-0381-6-23
    • (2013) BioData Min , vol.6 , pp. 23
    • Kim, D.1    Li, R.2    Dudek, S.M.3    Ritchie, M.D.4
  • 21
    • 84907058092 scopus 로고    scopus 로고
    • Knowledge-driven genomic interactions: an application in ovarian cancer
    • Kim, D., Li, R., Dudek, S. M., Frase, A. T., Pendergrass, S. A., and Ritchie, M. D. (2014). Knowledge-driven genomic interactions: an application in ovarian cancer. BioData Min. 7:20. doi: 10.1186/1756-0381-7-20
    • (2014) BioData Min , vol.7 , pp. 20
    • Kim, D.1    Li, R.2    Dudek, S.M.3    Frase, A.T.4    Pendergrass, S.A.5    Ritchie, M.D.6
  • 22
    • 85019734186 scopus 로고    scopus 로고
    • Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma
    • Kim, D., Li, R., Lucas, A., Verma, S. S., Dudek, S. M., and Ritchie, M. D. (2016). Using knowledge-driven genomic interactions for multi-omics data analysis: metadimensional models for predicting clinical outcomes in ovarian carcinoma. J. Am. Med. Inform. Assoc. 24, 577-587. doi: 10.1093/jamia/ocw165
    • (2016) J. Am. Med. Inform. Assoc , vol.24 , pp. 577-587
    • Kim, D.1    Li, R.2    Lucas, A.3    Verma, S.S.4    Dudek, S.M.5    Ritchie, M.D.6
  • 23
    • 84869875323 scopus 로고    scopus 로고
    • Synergistic effect of different levels of genomic data for cancer clinical outcome prediction
    • Kim, D., Shin, H., Song, Y. S., and Kim, J. H. (2012). Synergistic effect of different levels of genomic data for cancer clinical outcome prediction. J. Biomed. Inform. 45, 1191-1198. doi: 10.1016/j.jbi.2012.07.008
    • (2012) J. Biomed. Inform , vol.45 , pp. 1191-1198
    • Kim, D.1    Shin, H.2    Song, Y.S.3    Kim, J.H.4
  • 24
    • 84870796415 scopus 로고    scopus 로고
    • Bayesian correlated clustering to integrate multiple datasets
    • Kirk, P., Griffin, J. E., Savage, R. S., Ghahramani, Z., and Wild, D. L. (2012). Bayesian correlated clustering to integrate multiple datasets. Bioinformatics 28, 3290-3297. doi: 10.1093/bioinformatics/bts595
    • (2012) Bioinformatics , vol.28 , pp. 3290-3297
    • Kirk, P.1    Griffin, J.E.2    Savage, R.S.3    Ghahramani, Z.4    Wild, D.L.5
  • 25
    • 60849113429 scopus 로고    scopus 로고
    • Sparse canonical methods for biological data integration: application to a cross-platform study
    • Lê Cao, K.-A., Martin, P. G., Robert-Granié, C., and Besse, P. (2009). Sparse canonical methods for biological data integration: application to a cross-platform study. BMC Bioinform. 10:34. doi: 10.1186/1471-2105-10-34
    • (2009) BMC Bioinform , vol.10 , pp. 34
    • Lê Cao, K.-A.1    Martin, P.G.2    Robert-Granié, C.3    Besse, P.4
  • 28
    • 84867283138 scopus 로고    scopus 로고
    • Identifying multi-layer gene regulatory modules from multi-dimensional genomic data
    • Li, W., Zhang, S., Liu, C.-C., and Zhou, X. J. (2012). Identifying multi-layer gene regulatory modules from multi-dimensional genomic data. Bioinformatics 28, 2458-2466. doi: 10.1093/bioinformatics/bts476
    • (2012) Bioinformatics , vol.28 , pp. 2458-2466
    • Li, W.1    Zhang, S.2    Liu, C.-C.3    Zhou, X.J.4
  • 29
    • 84881181566 scopus 로고    scopus 로고
    • Group sparse canonical correlation analysis for genomic data integration
    • Lin, D., Zhang, J., Li, J., Calhoun, V. D., Deng, H.-W., and Wang, Y.-P. (2013). Group sparse canonical correlation analysis for genomic data integration. BMC Bioinform. 14:245. doi: 10.1186/1471-2105-14-245
    • (2013) BMC Bioinform , vol.14 , pp. 245
    • Lin, D.1    Zhang, J.2    Li, J.3    Calhoun, V.D.4    Deng, H.-W.5    Wang, Y.-P.6
  • 30
    • 84885617335 scopus 로고    scopus 로고
    • Bayesian consensus clustering
    • Lock, E. F., and Dunson, D. B. (2013). Bayesian consensus clustering. Bioinformatics 29, 2610-2616. doi: 10.1093/bioinformatics/btt425
    • (2013) Bioinformatics , vol.29 , pp. 2610-2616
    • Lock, E.F.1    Dunson, D.B.2
  • 31
    • 84876058478 scopus 로고    scopus 로고
    • Joint and individual variation explained (JIVE) for integrated analysis of multiple data types
    • Lock, E. F., Hoadley, K. A., Marron, J. S., and Nobel, A. B. (2013). Joint and individual variation explained (JIVE) for integrated analysis of multiple data types. Ann. Appl. Stat. 7, 523-542. doi: 10.1214/12-AOAS597
    • (2013) Ann. Appl. Stat , vol.7 , pp. 523-542
    • Lock, E.F.1    Hoadley, K.A.2    Marron, J.S.3    Nobel, A.B.4
  • 32
    • 79952606011 scopus 로고    scopus 로고
    • CNAmet: an R package for integrating copy number, methylation and expression data
    • Louhimo, R., and Hautaniemi, S. (2011). CNAmet: an R package for integrating copy number, methylation and expression data. Bioinformatics 27, 887-888. doi: 10.1093/bioinformatics/btr019
    • (2011) Bioinformatics , vol.27 , pp. 887-888
    • Louhimo, R.1    Hautaniemi, S.2
  • 33
    • 80455125871 scopus 로고    scopus 로고
    • Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles
    • Mankoo, P. K., Shen, R., Schultz, N., Levine, D. A., and Sander, C. (2011). Time to recurrence and survival in serous ovarian tumors predicted from integrated genomic profiles. PLoS ONE 6:e24709. doi: 10.1371/journal.pone.0024709
    • (2011) PLoS ONE , vol.6
    • Mankoo, P.K.1    Shen, R.2    Schultz, N.3    Levine, D.A.4    Sander, C.5
  • 34
    • 84875016233 scopus 로고    scopus 로고
    • Pattern discovery and cancer gene identification in integrated cancer genomic data
    • Mo, Q., Wang, S., Seshan, V. E., Olshen, A. B., Schultz, N., Sander, C., et al. (2013). Pattern discovery and cancer gene identification in integrated cancer genomic data. Proc. Natl. Acad. Sci. U.S.A. 110, 4245-4250. doi: 10.1073/pnas.1208949110
    • (2013) Proc. Natl. Acad. Sci. U.S.A , vol.110 , pp. 4245-4250
    • Mo, Q.1    Wang, S.2    Seshan, V.E.3    Olshen, A.B.4    Schultz, N.5    Sander, C.6
  • 35
    • 78751681684 scopus 로고    scopus 로고
    • Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme
    • Ovaska, K., Laakso, M., Haapa-Paananen, S., Louhimo, R., Chen, P., Aittomäki, V., et al. (2010). Large-scale data integration framework provides a comprehensive view on glioblastoma multiforme. Genome Med. 2:65. doi: 10.1186/gm186
    • (2010) Genome Med , vol.2 , pp. 65
    • Ovaska, K.1    Laakso, M.2    Haapa-Paananen, S.3    Louhimo, R.4    Chen, P.5    Aittomäki, V.6
  • 36
    • 62449336277 scopus 로고    scopus 로고
    • Sparse canonical correlation analysis with application to genomic data integration
    • Parkhomenko, E., Tritchler, D., and Beyene, J. (2009). Sparse canonical correlation analysis with application to genomic data integration. Stat. Appl. Genet. Mol. Biol. 8, 1-34. doi: 10.2202/1544-6115.1406
    • (2009) Stat. Appl. Genet. Mol. Biol , vol.8 , pp. 1-34
    • Parkhomenko, E.1    Tritchler, D.2    Beyene, J.3
  • 37
    • 84886425852 scopus 로고    scopus 로고
    • Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers
    • Rabin, B. A., Gaglio, B., Sanders, T., Nekhlyudov, L., Dearing, J. W., Bull, S., et al. (2013). Predicting cancer prognosis using interactive online tools: a systematic review and implications for cancer care providers. Cancer Epidemiol. Biomarkers Prev. 22, 1645-1656. doi: 10.1158/1055-9965.EPI-13-0513
    • (2013) Cancer Epidemiol. Biomarkers Prev , vol.22 , pp. 1645-1656
    • Rabin, B.A.1    Gaglio, B.2    Sanders, T.3    Nekhlyudov, L.4    Dearing, J.W.5    Bull, S.6
  • 38
    • 84900827183 scopus 로고    scopus 로고
    • Bayesian joint analysis of heterogeneous genomics data
    • Ray, P., Zheng, L., Lucas, J., and Carin, L. (2014). Bayesian joint analysis of heterogeneous genomics data. Bioinformatics 30, 1370-1376. doi: 10.1093/bioinformatics/btu064
    • (2014) Bioinformatics , vol.30 , pp. 1370-1376
    • Ray, P.1    Zheng, L.2    Lucas, J.3    Carin, L.4
  • 39
    • 84925031191 scopus 로고    scopus 로고
    • Methods of integrating data to uncover genotype-phenotype interactions
    • Ritchie, M. D., Holzinger, E. R., Li, R., Pendergrass, S. A., and Kim, D. (2015). Methods of integrating data to uncover genotype-phenotype interactions. Nat. Rev. Genet. 16, 85-97. doi: 10.1038/nrg3868
    • (2015) Nat. Rev. Genet , vol.16 , pp. 85-97
    • Ritchie, M.D.1    Holzinger, E.R.2    Li, R.3    Pendergrass, S.A.4    Kim, D.5
  • 40
    • 84953283238 scopus 로고    scopus 로고
    • Network-based integration of disparate omic data to identify "Silent Players" in cancer
    • Ruffalo, M., Koyutürk, M., and Sharan, R. (2015). Network-based integration of disparate omic data to identify "Silent Players" in cancer. PLOS Comput. Biol. 11:e1004595. doi: 10.1371/journal.pcbi.1004595
    • (2015) PLOS Comput. Biol , vol.11
    • Ruffalo, M.1    Koyutürk, M.2    Sharan, R.3
  • 41
  • 42
    • 84897864232 scopus 로고    scopus 로고
    • A pathway-based data integration framework for prediction of disease progression
    • Seoane, J. A., Day, I. N. M., Gaunt, T. R., and Campbell, C. (2014). A pathway-based data integration framework for prediction of disease progression. Bioinformatics 30, 838-845. doi: 10.1093/bioinformatics/btt610
    • (2014) Bioinformatics , vol.30 , pp. 838-845
    • Seoane, J.A.1    Day, I.N.M.2    Gaunt, T.R.3    Campbell, C.4
  • 43
    • 84859992638 scopus 로고    scopus 로고
    • Integrative subtype discovery in glioblastoma using iCluster
    • Shen, R., Mo, Q., Schultz, N., Seshan, V. E., Olshen, A. B., Huse, J., et al. (2012). Integrative subtype discovery in glioblastoma using iCluster. PLoS ONE 7:e35236. doi: 10.1371/journal.pone.0035236
    • (2012) PLoS ONE , vol.7
    • Shen, R.1    Mo, Q.2    Schultz, N.3    Seshan, V.E.4    Olshen, A.B.5    Huse, J.6
  • 44
    • 70449331456 scopus 로고    scopus 로고
    • Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis
    • Shen, R., Olshen, A. B., and Ladanyi, M. (2009). Integrative clustering of multiple genomic data types using a joint latent variable model with application to breast and lung cancer subtype analysis. Bioinformatics 25, 2906-2912. doi: 10.1093/bioinformatics/btp543
    • (2009) Bioinformatics , vol.25 , pp. 2906-2912
    • Shen, R.1    Olshen, A.B.2    Ladanyi, M.3
  • 45
    • 84931091190 scopus 로고    scopus 로고
    • Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery
    • Speicher, N. K., and Pfeifer, N. (2015). Integrating different data types by regularized unsupervised multiple kernel learning with application to cancer subtype discovery. Bioinformatics 31, i268-i275. doi: 10.1093/bioinformatics/btv244
    • (2015) Bioinformatics , vol.31
    • Speicher, N.K.1    Pfeifer, N.2
  • 46
    • 84862302350 scopus 로고    scopus 로고
    • 'Hierarchical beta processes and the indian buffet process,'
    • (AISTATS) (San Juan)
    • Thibaux, R., and Jordan, M. I. (2007). "Hierarchical beta processes and the indian buffet process," in Artificial Intelligence and Statistics (AISTATS) (San Juan), 564-571
    • (2007) Artificial Intelligence and Statistics , pp. 564-571
    • Thibaux, R.1    Jordan, M.I.2
  • 47
    • 85194972808 scopus 로고
    • Regression shrinkage and selection via the lasso
    • Tibshirani, R. (1994). Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 58, 267-288
    • (1994) J. R. Stat. Soc. Ser. B , vol.58 , pp. 267-288
    • Tibshirani, R.1
  • 48
    • 0001224048 scopus 로고    scopus 로고
    • Sparse Bayesian learning and the relevance vector machine
    • Tipping, M. E. (2001). Sparse Bayesian learning and the relevance vector machine. J. Mach. Learn. Res. 1, 211-244. doi: 10.1162/15324430152748236
    • (2001) J. Mach. Learn. Res , vol.1 , pp. 211-244
    • Tipping, M.E.1
  • 49
    • 18244409687 scopus 로고    scopus 로고
    • Gene expression profiling predicts clinical outcome of breast cancer
    • Van 't Veer, L. J., Dai, H., van de Vijver, M. J., He, Y. D., Hart, A. A. M., Mao, M., et al. (2002). Gene expression profiling predicts clinical outcome of breast cancer. Nature 415, 530-536. doi: 10.1038/415530a
    • (2002) Nature , vol.415 , pp. 530-536
    • Van 't Veer, L.J.1    Dai, H.2    van de Vijver, M.J.3    He, Y.D.4    Hart, A.A.M.5    Mao, M.6
  • 50
    • 77954195272 scopus 로고    scopus 로고
    • Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM
    • Vaske, C. J., Benz, S. C., Sanborn, J. Z., Earl, D., Szeto, C., Zhu, J., et al. (2010). Inference of patient-specific pathway activities from multi-dimensional cancer genomics data using PARADIGM. Bioinformatics 26, i237-i245. doi: 10.1093/bioinformatics/btq182
    • (2010) Bioinformatics , vol.26
    • Vaske, C.J.1    Benz, S.C.2    Sanborn, J.Z.3    Earl, D.4    Szeto, C.5    Zhu, J.6
  • 51
    • 84895516704 scopus 로고    scopus 로고
    • Similarity network fusion for aggregating data types on a genomic scale
    • Wang, B., Mezlini, A. M., Demir, F., Fiume, M., Tu, Z., Brudno, M., et al. (2014). Similarity network fusion for aggregating data types on a genomic scale. Nat. Methods 11, 333-337. doi: 10.1038/nmeth.2810
    • (2014) Nat. Methods , vol.11 , pp. 333-337
    • Wang, B.1    Mezlini, A.M.2    Demir, F.3    Fiume, M.4    Tu, Z.5    Brudno, M.6
  • 52
    • 84983543817 scopus 로고    scopus 로고
    • Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer
    • Wei, R., De Vivo, I., Huang, S., Zhu, X., Risch, H., Moore, J. H., et al. (2016). Meta-dimensional data integration identifies critical pathways for susceptibility, tumorigenesis and progression of endometrial cancer. Oncotarget 7, 55249-55263. doi: 10.18632/oncotarget.10509
    • (2016) Oncotarget , vol.7 , pp. 55249-55263
    • Wei, R.1    De Vivo, I.2    Huang, S.3    Zhu, X.4    Risch, H.5    Moore, J.H.6
  • 54
    • 68249115586 scopus 로고    scopus 로고
    • Extensions of sparse canonical correlation analysis with applications to genomic data
    • Witten, D. M., and Tibshirani, R. J. (2009). Extensions of sparse canonical correlation analysis with applications to genomic data. Stat. Appl. Genet. Mol. Biol. 8, 1-27. doi: 10.2202/1544-6115.1470
    • (2009) Stat. Appl. Genet. Mol. Biol , vol.8 , pp. 1-27
    • Witten, D.M.1    Tibshirani, R.J.2
  • 55
    • 77955106017 scopus 로고    scopus 로고
    • A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network
    • You, Z.-H., Yin, Z., Han, K., Huang, D.-S., and Zhou, X. (2010). A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties of functional gene network. BMC Bioinform. 11:343. doi: 10.1186/1471-2105-11-343
    • (2010) BMC Bioinform , vol.11 , pp. 343
    • You, Z.-H.1    Yin, Z.2    Han, K.3    Huang, D.-S.4    Zhou, X.5
  • 56
    • 80055083654 scopus 로고    scopus 로고
    • Patient-specific data fusion defines prognostic cancer subtypes
    • Yuan, Y., Savage, R. S., and Markowetz, F. (2011). Patient-specific data fusion defines prognostic cancer subtypes. PLoS Comput. Biol. 7:e1002227. doi: 10.1371/journal.pcbi.1002227
    • (2011) PLoS Comput. Biol , vol.7
    • Yuan, Y.1    Savage, R.S.2    Markowetz, F.3
  • 57
    • 79959448071 scopus 로고    scopus 로고
    • A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules
    • Zhang, S., Li, Q., Liu, J., and Zhou, X. J. (2011). A novel computational framework for simultaneous integration of multiple types of genomic data to identify microRNA-gene regulatory modules. Bioinformatics 27, i401-i409. doi: 10.1093/bioinformatics/btr206
    • (2011) Bioinformatics , vol.27
    • Zhang, S.1    Li, Q.2    Liu, J.3    Zhou, X.J.4
  • 58
    • 84868152524 scopus 로고    scopus 로고
    • Discovery of multi-dimensional modules by integrative analysis of cancer genomic data
    • Zhang, S., Liu, C.-C., Li, W., Shen, H., Laird, P. W., and Zhou, X. J. (2012). Discovery of multi-dimensional modules by integrative analysis of cancer genomic data. Nucleic Acids Res. 40, 9379-9391. doi: 10.1093/nar/gks725
    • (2012) Nucleic Acids Res , vol.40 , pp. 9379-9391
    • Zhang, S.1    Liu, C.-C.2    Li, W.3    Shen, H.4    Laird, P.W.5    Zhou, X.J.6
  • 59
    • 84863769781 scopus 로고    scopus 로고
    • A Bayesian approach to discovering truth from conflicting sources for data integration
    • Zhao, B., Rubinstein, B. I. P., Gemmell, J., and Han, J. (2012). A Bayesian approach to discovering truth from conflicting sources for data integration. Proc. VLDB Endow. 5, 550-561. doi: 10.14778/2168651.2168656
    • (2012) Proc. VLDB Endow , vol.5 , pp. 550-561
    • Zhao, B.1    Rubinstein, B.I.P.2    Gemmell, J.3    Han, J.4


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.