Artificial Intelligence in Medicine
Volume 48, Issue 2 , Pages 119-127 , February 2010

Method of regulatory network that can explore protein regulations for disease classification

  • Hong Qiang Wang

      Affiliations

    • Research Institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
  • ,
  • Hai Long Zhu

      Affiliations

    • Research Institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong
    • Corresponding Author InformationCorresponding author at: W502, Research Institute of Innovative Products and Technologies, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong. Tel.: +852 34003520; fax: +852 27640011.
  • ,
  • William C.S. Cho

      Affiliations

    • Department of Clinical Oncology, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong
  • ,
  • Timothy T.C. Yip

      Affiliations

    • Department of Clinical Oncology, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong
  • ,
  • Roger K.C. Ngan

      Affiliations

    • Department of Clinical Oncology, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong
  • ,
  • Stephen C.K. Law

      Affiliations

    • Department of Clinical Oncology, Queen Elizabeth Hospital, 30 Gascoigne Road, Kowloon, Hong Kong

Received 2 September 2008 ,Revised 8 July 2009 ,Accepted 20 July 2009.

References 

  1. Schena M, Shalon D, Davis RW, Brown PO. Quantitative monitoring of gene expression patterns with a complementary microarray. Science. 1995;270(5235):467–470
  2. Fetsch PA, Simone NL, Bryant-Greenwood PK, Marincola FM, Filie AC, Petricoin EF, et al. Proteomic evaluation of archival cytologic material using SELDI affinity mass spectrometry: potential for diagnostic applications. American Journal of Clinical Pathology. 2002;118(6):870–876
  3. Banerjee H, Hawkins Z, Williams J, Blackshear M, Sawyer C, Cezares L, et al. Search for a novel biomarker for the brain cancer astrocytoma by using surface enhanced laser desorption/ionisation (SELDI) technique. Cellular and Molecular Biology (Noisy-le-grand). 2004;50(6):733–736
  4. Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, Mesirov JP, et al. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science. 1999;286(5439):531–537
  5. Wright G, Tan B, Rosenwald A, Hurt EH, Wiestner A, Staudt LM. A gene expression-based method to diagnose clinically distinct subgroups of diffuse large B cell lymphoma. Proceedings of the National Academy of Sciences. 2003;100(17):9991–9996
  6. Wu DL, Wang WJ, Guan M, Jin SB, Jin CR, Zhang YF. Screening urine markers of renal cell carcinoma using SELDI-TOF-MS. Zhonghua Yi Xue Za Zhi. 2004;84(13):1092–1095
  7. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z. Tissue classification with gene expression profiles. Journal of Computational Biology. 2000;7(3–4):559–583
  8. Nicolau M, Tibshirani R, Børresen-Dale AL, Jeffrey SS. Disease-specific genomic analysis: identifying the signature of pathologic biology. Bioinformatics. 2007;23(8):957–965
  9. Kim SY, Imoto S, Miyano S. Inferring gene networks from time series microarray data using dynamic Bayesian networks. Briefings in Bioinformatics. 2003;4(3):228–235
  10. Zou M, Conzen SD. A new dynamic Bayesian network (DBN) approach for identifying gene regulatory networks from time course microarray data. Bioinformatics. 2005;21(1):71–79
  11. Yamaguchi R, Yoshida R, Imoto S, Higuchi T, Miyano S. Finding module-based gene networks with state-space models—mining high-dimensional and short time-course gene expression data. IEEE Transaction on Signal Processing Magazine. 2007;24:37–46
  12. Qiu P, Wang ZJ, Liu KJR. Ensemble dependence model for classification and prediction of cancer and normal gene expression data. Bioinformatics. 2005;21(14):3114–3121
  13. Antonov AV, Tetko IV, Mader MT, Budczies J, Mewes HW. Optimization models for cancer classification: extracting gene interaction information from microarray expression data. Bioinformatics. 2004;20(5):644–652
  14. Curtis RK, Brand MD. Analysing microarray data using modular regulation analysis. Bioinformatics. 2004;20:1272–1284
  15. Yeung LK, Szeto LK, Liew AWC, Yan H. Dominant spectral component analysis for transcriptional regulations using microarray time-series data. Bioinformatics. 2004;20(5):742–749
  16. Tlsty T. Cancer: whispering sweet somethings. Nature. 2008;453(7195):604–605
  17. Segal E, Friedman N, Kaminski N, Regev A, Koller D. From signatures to models: understanding cancer using microarrays. Nature Genetics. 2005;37:S38–S45
  18. Calvano SE, Xiao W, Richards DR, Felciano RM, Baker HV, Cho RJ, et al. A network-based analysis of systemic inflammation in humans. Nature. 2005;437:1032–1037
  19. Cho WCS, Yip TTC, Yip C, Yip V, Thulasiraman V, Ngan RKC, et al. Identification of serum amyloid A protein As a potentially useful biomarker to monitor relapse of nasopharyngeal cancer by serum proteomic profiling. Clinical Cancer Research. 2004;10:43–52
  20. Yip TTC, Chan JWM, Cho WCS, Yip TT, Wang Z, Kwan TL, et al. Protein chip array profiling analysis in patients with severe acute respiratory syndrome identified serum amyloid A protein as a biomarker potentially useful in monitoring the extent of pneumonia. Clinical Chemistry. 2005;51:47–55
  21. Hopfield J. Neural networks and physical systems with emergent collective computational abilities. Proceedings of the National Academy of Sciences. 1982;79(8):2554–2558
  22. Wang HQ, Huang DS. Regulation probability method for gene selection. Pattern Recognition Letter. 2006;27:116–122
  23. Wang HQ, Wong HS, Huang DS, Shu J. Extracting gene regulation information for cancer classification. Pattern Recognition. 2007;40:3379–3392
  24. Carter SL, Brechbuhler CM, Griffin M, Bond AT. Gene co-expression network topology provides a framework for molecular characterization of cellular state. Bioinformatics. 2004;20(14):2242–2250
  25. Wang H. Nearest neighbors by neighborhood counting. Transactions on Pattern Analysis and Machine Intelligence. 2006;28(6):942–953
  26. Pochet N, Smet FD, Suykens JA, Moor DB. Systematic benchmarking of microarray data classification: assessing the role of non-linearity and dimensionality reduction. Bioinformatics. 2004;20:3185–3195
  27. Guyon I, Weston J, Barnhill S, Vapnik V. Gene selection for cancer classification using support vector machines. Machine Learning. 2002;46(1–3):389–422
  28. Khan J, Wei JS, Ringner M, Saal LH, Landanyi M, Westermann F, et al. Classification and diagnostic prediction of cancers using gene expression profiling and artificial neural networks. Nature Medicine. 2001;7:670–673
  29. Furey TS, Cristianini N, Duffy N, Bednarski D, Schummer M, Haussler D. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics. 2000;16(10):906–914
  30. Scholkopf B, Sung KK, Burges CJC, Girosi F, Niyogi P, Poggio T, et al. Comparing support vector machines with Gaussian kernels to radial basis function classifiers. IEEE Transactions on Signal Processing. 1997;45(11):2758–2765

PII: S0933-3657(09)00104-3

doi: 10.1016/j.artmed.2009.07.011

Artificial Intelligence in Medicine
Volume 48, Issue 2 , Pages 119-127 , February 2010