메뉴 건너뛰기




Volumn 8, Issue 3, 2013, Pages

Classification of Time Series Gene Expression in Clinical Studies via Integration of Biological Network

Author keywords

[No Author keywords available]

Indexed keywords

ACCURACY; ARTICLE; CLASSIFICATION ALGORITHM; CLINICAL STUDY; DATA ANALYSIS; GENE EXPRESSION; GENE FUNCTION; HIDDEN MARKOV MODEL; K NEAREST NEIGHBOR; MATHEMATICAL COMPUTING; MATHEMATICAL MODEL; PREDICTION; PROTEIN PROTEIN INTERACTION; RELIABILITY; SUPPORT VECTOR MACHINE; TIME SERIES ANALYSIS;

EID: 84874903469     PISSN: None     EISSN: 19326203     Source Type: Journal    
DOI: 10.1371/journal.pone.0058383     Document Type: Article
Times cited : (10)

References (55)
  • 1
    • 69949164525 scopus 로고    scopus 로고
    • Integrating genomics, proteomics and bioinformatics in translational studies of molecular medicine
    • Ostrowski J, Wyrwicz LS, (2009) Integrating genomics, proteomics and bioinformatics in translational studies of molecular medicine. Expert review of molecular diagnostics 9: 623-630.
    • (2009) Expert Review of Molecular Diagnostics , vol.9 , pp. 623-630
    • Ostrowski, J.1    Wyrwicz, L.S.2
  • 4
    • 33645221975 scopus 로고    scopus 로고
    • Gene expression patterns for doxorubicin (Adriamycin) and cyclophosphamide (cytoxan)(AC) response and resistance
    • Cleator S, Tsimelzon A, Ashworth A, Dowsett M, Dexter T, et al. (2006) Gene expression patterns for doxorubicin (Adriamycin) and cyclophosphamide (cytoxan)(AC) response and resistance. Breast cancer research and treatment 95: 229-233.
    • (2006) Breast Cancer Research and Treatment , vol.95 , pp. 229-233
    • Cleator, S.1    Tsimelzon, A.2    Ashworth, A.3    Dowsett, M.4    Dexter, T.5
  • 5
    • 33749030177 scopus 로고    scopus 로고
    • Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer
    • Hess KR, Anderson K, Symmans WF, Valero V, Ibrahim N, et al. (2006) Pharmacogenomic predictor of sensitivity to preoperative chemotherapy with paclitaxel and fluorouracil, doxorubicin, and cyclophosphamide in breast cancer. Journal of clinical oncology 24: 4236-4244.
    • (2006) Journal of Clinical Oncology , vol.24 , pp. 4236-4244
    • Hess, K.R.1    Anderson, K.2    Symmans, W.F.3    Valero, V.4    Ibrahim, N.5
  • 7
    • 80755169537 scopus 로고    scopus 로고
    • Unsupervised detection of genes of influence in lung cancer using biological networks
    • Goldenberg A, Mostafavi S, Quon G, Boutros PC, Morris QD, (2011) Unsupervised detection of genes of influence in lung cancer using biological networks. Bioinformatics 27: 3166-3172.
    • (2011) Bioinformatics , vol.27 , pp. 3166-3172
    • Goldenberg, A.1    Mostafavi, S.2    Quon, G.3    Boutros, P.C.4    Morris, Q.D.5
  • 9
    • 59849125136 scopus 로고    scopus 로고
    • Dynamic modularity in protein interaction networks predicts breast cancer outcome
    • Taylor IW, Linding R, Warde-Farley D, Liu Y, Pesquita C, et al. (2009) Dynamic modularity in protein interaction networks predicts breast cancer outcome. Nature biotechnology 27: 199-204.
    • (2009) Nature Biotechnology , vol.27 , pp. 199-204
    • Taylor, I.W.1    Linding, R.2    Warde-Farley, D.3    Liu, Y.4    Pesquita, C.5
  • 10
    • 77957694184 scopus 로고    scopus 로고
    • Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network
    • Junjie S, Byung-Jun Y, Edward D (2010) Identification of diagnostic subnetwork markers for cancer in human protein-protein interaction network. BMC Bioinformatics 11.
    • (2010) BMC Bioinformatics , vol.11
    • Junjie, S.1    Byung-Jun, Y.2    Edward, D.3
  • 11
    • 79959392210 scopus 로고    scopus 로고
    • Optimally discriminative subnetwork markers predict response to chemotherapy
    • Dao P, Wang K, Collins C, Ester M, Lapuk A, et al. (2011) Optimally discriminative subnetwork markers predict response to chemotherapy. Bioinformatics 27: i205-i213.
    • (2011) Bioinformatics , vol.27
    • Dao, P.1    Wang, K.2    Collins, C.3    Ester, M.4    Lapuk, A.5
  • 12
    • 46249094886 scopus 로고    scopus 로고
    • Alignment and classification of time series gene expression in clinical studies
    • Lin T, Kaminski N, Bar-Joseph Z, (2008) Alignment and classification of time series gene expression in clinical studies. Bioinformatics 24: i147-i155.
    • (2008) Bioinformatics , vol.24
    • Lin, T.1    Kaminski, N.2    Bar-Joseph, Z.3
  • 13
    • 34248363540 scopus 로고    scopus 로고
    • A patient-gene model for temporal expression profiles in clinical studies
    • Kaminski N, Bar-Joseph Z, (2007) A patient-gene model for temporal expression profiles in clinical studies. Journal of Computational Biology 14: 324-338.
    • (2007) Journal of Computational Biology , vol.14 , pp. 324-338
    • Kaminski, N.1    Bar-Joseph, Z.2
  • 14
    • 84934442362 scopus 로고    scopus 로고
    • Computational diagnostics with gene expression profiles
    • Lottaz C, Kostka D, Markowetz F, Spang R, (2008) Computational diagnostics with gene expression profiles. Methods Mol Biol 453: 281-296.
    • (2008) Methods Mol Biol , vol.453 , pp. 281-296
    • Lottaz, C.1    Kostka, D.2    Markowetz, F.3    Spang, R.4
  • 15
    • 0036789860 scopus 로고    scopus 로고
    • Assessment of different treatment failure criteria in a cohort of relapsing-remitting multiple sclerosis patients treated with interferon β: Implications for clinical trials
    • Río J, Nos C, Tintoré M, Borrás C, Galán I, et al. (2002) Assessment of different treatment failure criteria in a cohort of relapsing-remitting multiple sclerosis patients treated with interferon β: Implications for clinical trials. Annals of neurology 52: 400-406.
    • (2002) Annals of Neurology , vol.52 , pp. 400-406
    • Río, J.1    Nos, C.2    Tintoré, M.3    Borrás, C.4    Galán, I.5
  • 16
    • 10344225070 scopus 로고    scopus 로고
    • Strategies for managing the side effects of treatments for multiple sclerosis
    • Langer-Gould A, Moses HH, Murray TJ, (2004) Strategies for managing the side effects of treatments for multiple sclerosis. Neurology 63: S35-S41.
    • (2004) Neurology , vol.63
    • Langer-Gould, A.1    Moses, H.H.2    Murray, T.J.3
  • 17
    • 20044373640 scopus 로고    scopus 로고
    • Transcription-based prediction of response to IFNβ using supervised computational methods
    • Baranzini SE, Mousavi P, Rio J, Caillier SJ, Stillman A, et al. (2004) Transcription-based prediction of response to IFNβ using supervised computational methods. PLoS Biology 3: e2.
    • (2004) PLoS Biology , vol.3
    • Baranzini, S.E.1    Mousavi, P.2    Rio, J.3    Caillier, S.J.4    Stillman, A.5
  • 18
    • 77949639406 scopus 로고    scopus 로고
    • Long-term genome-wide blood RNA expression profiles yield novel molecular response candidates for IFN-β-1b treatment in relapsing remitting MS
    • Goertsches RH, Hecker M, Koczan D, Serrano-Fernandez P, Moeller S, et al. (2010) Long-term genome-wide blood RNA expression profiles yield novel molecular response candidates for IFN-β-1b treatment in relapsing remitting MS. Pharmacogenomics 11: 147-161.
    • (2010) Pharmacogenomics , vol.11 , pp. 147-161
    • Goertsches, R.H.1    Hecker, M.2    Koczan, D.3    Serrano-Fernandez, P.4    Moeller, S.5
  • 22
    • 0024610919 scopus 로고
    • A tutorial on hidden Markov models and selected applications in speech recognition
    • Rabiner LR, (1989) A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE 77: 257-286.
    • (1989) Proceedings of the IEEE , vol.77 , pp. 257-286
    • Rabiner, L.R.1
  • 23
    • 4944252468 scopus 로고    scopus 로고
    • Using hidden Markov models to analyze gene expression time course data
    • Schliep A, Schönhuth A, Steinhoff C, (2003) Using hidden Markov models to analyze gene expression time course data. Bioinformatics 19: i255-i263.
    • (2003) Bioinformatics , vol.19
    • Schliep, A.1    Schönhuth, A.2    Steinhoff, C.3
  • 27
    • 14644416505 scopus 로고    scopus 로고
    • Identifying time-lagged gene clusters using gene expression data
    • Ji L, Tan KL, (2005) Identifying time-lagged gene clusters using gene expression data. Bioinformatics 21: 509-516.
    • (2005) Bioinformatics , vol.21 , pp. 509-516
    • Ji, L.1    Tan, K.L.2
  • 32
    • 34249753618 scopus 로고
    • Support-vector networks
    • Cortes C, Vapnik V, (1995) Support-vector networks. Machine learning 20: 273-297.
    • (1995) Machine Learning , vol.20 , pp. 273-297
    • Cortes, C.1    Vapnik, V.2
  • 36
    • 84863349895 scopus 로고    scopus 로고
    • High Accordance in Prognosis Prediction of Colorectal Cancer across Independent Datasets by Multi-Gene Module Expression Profiles
    • Li W, Wang R, Yan Z, Bai L, Sun Z, (2012) High Accordance in Prognosis Prediction of Colorectal Cancer across Independent Datasets by Multi-Gene Module Expression Profiles. Plos One 7: e33653.
    • (2012) Plos One , vol.7
    • Li, W.1    Wang, R.2    Yan, Z.3    Bai, L.4    Sun, Z.5
  • 37
    • 4644335402 scopus 로고    scopus 로고
    • Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine
    • Weston AD, Hood L, (2004) Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. Journal of proteome research 3: 179-196.
    • (2004) Journal of Proteome Research , vol.3 , pp. 179-196
    • Weston, A.D.1    Hood, L.2
  • 38
    • 1842422544 scopus 로고    scopus 로고
    • Comparison of mRNA gene expression by RT-PCR and DNA microarray
    • Etienne W, Meyer MH, Peppers J, Meyer RA, (2004) Comparison of mRNA gene expression by RT-PCR and DNA microarray. Biotechniques 36: 618-627.
    • (2004) Biotechniques , vol.36 , pp. 618-627
    • Etienne, W.1    Meyer, M.H.2    Peppers, J.3    Meyer, R.A.4
  • 39
    • 0141450348 scopus 로고    scopus 로고
    • Comparison and meta-analysis of microarray data: from the bench to the computer desk
    • Moreau Y, Aerts S, Moor BD, Strooper BD, Dabrowski M, (2003) Comparison and meta-analysis of microarray data: from the bench to the computer desk. TRENDS in Genetics 19: 570-577.
    • (2003) TRENDS in Genetics , vol.19 , pp. 570-577
    • Moreau, Y.1    Aerts, S.2    Moor, B.D.3    Strooper, B.D.4    Dabrowski, M.5
  • 40
    • 25444486426 scopus 로고    scopus 로고
    • Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR-how well do they correlate?
    • Dallas P, Gottardo N, Firth M, Beesley A, Hoffmann K, et al. (2005) Gene expression levels assessed by oligonucleotide microarray analysis and quantitative real-time RT-PCR-how well do they correlate? Bmc Genomics 6: 59.
    • (2005) Bmc Genomics , vol.6 , pp. 59
    • Dallas, P.1    Gottardo, N.2    Firth, M.3    Beesley, A.4    Hoffmann, K.5
  • 41
    • 80051675637 scopus 로고    scopus 로고
    • Classification of clinical gene-sample-time microarray expression data via tensor decomposition methods
    • Li Y, Ngom A (2011) Classification of clinical gene-sample-time microarray expression data via tensor decomposition methods. Computational Intelligence Methods for Bioinformatics and Biostatistics: 275-286.
    • (2011) Computational Intelligence Methods for Bioinformatics and Biostatistics , pp. 275-286
    • Li, Y.1    Ngom, A.2
  • 42
  • 43
    • 66349133214 scopus 로고    scopus 로고
    • Constrained mixture estimation for analysis and robust classification of clinical time series
    • Costa IG, Schönhuth A, Hafemeister C, Schliep A, (2009) Constrained mixture estimation for analysis and robust classification of clinical time series. Bioinformatics 25: i6.
    • (2009) Bioinformatics , vol.25
    • Costa, I.G.1    Schönhuth, A.2    Hafemeister, C.3    Schliep, A.4
  • 44
    • 36249003318 scopus 로고    scopus 로고
    • Class prediction from time series gene expression profiles using dynamical systems kernels
    • Borgwardt K, Vishwanathan S, Kriegel H (2006) Class prediction from time series gene expression profiles using dynamical systems kernels. Pacific Symposium on Biocomputing. pp. 547.
    • (2006) Pacific Symposium on Biocomputing , pp. 547
    • Borgwardt, K.1    Vishwanathan, S.2    Kriegel, H.3
  • 46
    • 0035575568 scopus 로고    scopus 로고
    • Personalized medicine: revolutionizing drug discovery and patient care
    • Ginsburg GS, McCarthy JJ, (2001) Personalized medicine: revolutionizing drug discovery and patient care. TRENDS in Biotechnology 19: 491-496.
    • (2001) TRENDS in Biotechnology , vol.19 , pp. 491-496
    • Ginsburg, G.S.1    McCarthy, J.J.2
  • 47
    • 77949493376 scopus 로고    scopus 로고
    • Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles
    • Huang T, Cui WR, Hu LL, Feng KY, Li YX, et al. (2009) Prediction of pharmacological and xenobiotic responses to drugs based on time course gene expression profiles. Plos One 4: e8126.
    • (2009) Plos One , vol.4
    • Huang, T.1    Cui, W.R.2    Hu, L.L.3    Feng, K.Y.4    Li, Y.X.5
  • 48
    • 44449110026 scopus 로고    scopus 로고
    • Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept
    • Koczan D, Drynda S, Hecker M, Drynda A, Guthke R, et al. (2008) Molecular discrimination of responders and nonresponders to anti-TNFalpha therapy in rheumatoid arthritis by etanercept. Arthritis Res Ther 10: R50.
    • (2008) Arthritis Res Ther , vol.10
    • Koczan, D.1    Drynda, S.2    Hecker, M.3    Drynda, A.4    Guthke, R.5
  • 49
    • 42249090564 scopus 로고    scopus 로고
    • Optimization of treatment with interferon beta in multiple sclerosis. Usefulness of automatic system application criteria
    • Ruiz-Peña JL, Duque P, Izquierdo G, (2008) Optimization of treatment with interferon beta in multiple sclerosis. Usefulness of automatic system application criteria. BMC neurology 8: 3.
    • (2008) BMC Neurology , vol.8 , pp. 3
    • Ruiz-Peña, J.L.1    Duque, P.2    Izquierdo, G.3
  • 51
    • 33750719994 scopus 로고    scopus 로고
    • Global systems biology, personalized medicine and molecular epidemiology
    • Nicholson JK (2006) Global systems biology, personalized medicine and molecular epidemiology. Molecular systems biology 2.
    • (2006) Molecular systems biology , vol.2
    • Nicholson, J.K.1
  • 52
    • 0242287052 scopus 로고    scopus 로고
    • Statistical methods for identifying differentially expressed genes in DNA microarrays
    • Storey JD, Tibshirani R, (2003) Statistical methods for identifying differentially expressed genes in DNA microarrays. Methods in molecular biology (Clifton, NJ) 224: 149-158.
    • (2003) Methods in Molecular Biology (Clifton, NJ) , vol.224 , pp. 149-158
    • Storey, J.D.1    Tibshirani, R.2
  • 53
    • 0037172724 scopus 로고    scopus 로고
    • A prediction-based resampling method for estimating the number of clusters in a dataset
    • research0036
    • Dudoit S, Fridlyand J, (2002) A prediction-based resampling method for estimating the number of clusters in a dataset. Genome biology 3: research0036.
    • (2002) Genome Biology , vol.3
    • Dudoit, S.1    Fridlyand, J.2


* 이 정보는 Elsevier사의 SCOPUS DB에서 KISTI가 분석하여 추출한 것입니다.