New cancer diagnosis modeling using boosting and projective adaptive resonance theory with improved reliable index

Biochemical Engineering Journal

Volumn 33, Issue 2, 2007, Pages 100-109

  Takahashi, Hiro ^a,b,c   Murase, Yasuyuki ^a   Kobayashi, Takeshi ^d   Honda, Hiroyuki ^a  

^a NAGOYA UNIVERSITY   (Japan)

^b National Cancer Center Research Institute ^*

^c NATIONAL CANCER CENTER RESEARCH INSTITUTE   (Japan)

^d CHUBU UNIVERSITY   (Japan)

Author keywords

Cancer diagnosis; Fuzzy classifier; Marker gene selection; Projective adaptive resonance theory; Reliability index

Indexed keywords

ALGORITHMS; BIOMARKERS; DNA; FUZZY SETS; GENETIC ENGINEERING; PATIENT TREATMENT;

CANCER DIAGNOSTIC PREDICTION; FUZZY CLASSIFIERS; GENE EXPRESSION; MARKER GENE SELECTION; PROJECTIVE ADAPTIVE RESONANCE THEORY; RELIABILITY INDEX;

ONCOLOGY;

ACUTE LEUKEMIA; ALGORITHM; ARTICLE; BOOSTED FUZZY CLASSIFIER WITH SWEEP OPERATOR; BRAIN TUMOR; CANCER DIAGNOSIS; CLINICAL TRIAL; DIAGNOSTIC ACCURACY; DISEASE MARKER; HUMAN; INTERMETHOD COMPARISON; MAJOR CLINICAL STUDY; MARKER GENE; MICROARRAY ANALYSIS; NUCLEOTIDE SEQUENCE; PREDICTIVE VALIDITY; PRIORITY JOURNAL; PROGNOSIS; PROJECTIVE ADAPTIVE RESONANCE THEORY; RELIABILITY; TUMOR GENE;

EID: 33846504413     PISSN: 1369703X     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.bej.2006.08.004     Document Type: Article

References (23)

1. Ando T., Suguro M., Hanai T., Kobayashi T., Honda H., and Seto M. Fuzzy neural network applied to gene expression profiling for predicting the prognosis of diffuse large B-cell lymphoma. Jpn. J. Cancer Res. 93 (2002) 1207-1212
2. Guyon I., Weston J., Barnhill S., and Vapnik V. Gene selection for cancer classification using support vector machines. Mach. Learn. 46 (2002) 389-422
3. Takahashi H., and Honda H. A new reliable cancer diagnosis method using boosted fuzzy classifier with SWEEP operator method. J. Chem. Eng. Jpn. 38 (2005) 763-773
4. Golub T.R., Slonim D.K., Tamayo P., Huard C., Gaasenbeek M., Mesirov J.P., Coller H., Loh M.L., Downing J.R., Caligiuri M.A., Bloomfield C.D., and Lander E.S. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286 (1999) 531-537
5. Takahashi H., Kobayashi T., and Honda H. Construction of robust prognostic predictors by using projective adaptive resonance theory as a gene filtering method. Bioinformatics 21 (2005) 179-186
6. Park D.J., Vuong P.T., de Vos S., Douter D., and Koeffler H.P. Comparative analysis of genes regulated by PML/RAR alpha and PLZF/RAR alpha in response to retinoic acid using oligonucleotide arrays. Blood 102 (2003) 3727-3736
7. Pomeroy S.L., Tamayo P., Gaasenbeek M., Sturla L.M., Angelo M., McLaughlin M.E., Kim J.Y., Goumnerova L.C., Black P.M., Lau C., Allen J.C., Zagzag D., Olson J.M., Curran T., Wetmore C., Biegel J.A., Poggio T., Mukherjee S., Rifkin R., Califano A., Stolovitzky G., Louis D.N., Mesirov J.P., Lander E.S., and Golub T.R. Prediction of central nervous system embryonal tumour outcome based on gene expression. Nature 415 (2002) 436-442
8. Schapire R.E. The strength of weak learnability. Mach. Learn. 5 (1990) 197-227
9. Freund Y., and Schapire R. A decision-theoretic generalization of online learning and an application to boosting. J. Comput. Syst. Sci. 55 (1997) 119-139
10. Freund Y. Adaptive version of the boost by majority algorithm. Mach. Learn. 43 (2000) 293-318
11. Friedman J., Hastie T., and Tibshirani R. Additive logistic regression: a statistical view of boosting. Ann. Stat. 28 (2000) 337-407
12. Horikawa S., Furuhashi T., and Uchikawa Y. On fuzzy modeling using fuzzy neural networks with the back-propagation algorithm. IEEE Trans. Neural Networ. 3 (1992) 801-806
13. Huang Y., and Li Y. Prediction of protein subcellular locations using fuzzy k-NN method. Bioinformatics 20 (2004) 21-28
14. Vapnik V.N., and Chervonenkis A. A note on one class of perceptrons. Automat. Rem. Control 25 (1964) 821-837
15. Joachims T. Making Large-scale SVM Learning Practical (1999), MIT Press, Cambridge
16. Noguchi H., Hanai T., Takahashi W., Ichii T., Tanikawa M., Masuoka S., Honda H., and Kobayashi T. Model construction for quality of beer and brewing process using FNN. Kagaku Kougaku Ronbunshu 25 (1999) 695-701
17. Noguchi H., Hanai T., Honda H., Harrison L.C., and Kobayashi T. Fuzzy neural network-based prediction of the motif for MHC class II binding peptides. J. Biosci. Bioeng. 92 (2001) 227-231
18. Cao Y., and Wu J. Projective ART for clustering data sets in high dimensional spaces. Neural Networ. 15 (2002) 105-120
19. Cao Y., and Wu J. Dynamics of projective adaptive resonance theory model: the foundation of PART algorithm. IEEE Trans. Neural Networ. 15 (2004) 245-260
20. Tan P.G., Xing Z., and Li Z.Q. Expression of cyclin D1 in brain gliomas and its significance. Ai Zheng 23 (2004) 63-65
21. Akervall J., Kurnit D.M., Adams M., Zhu S., Fisher S.G., Bradford C.R., and Carey T.E. Overexpression of cyclin D1 correlates with sensitivity to cisplatin in squamous cell carcinoma cell lines of the head and neck. Acta Otolaryngol. 124 (2004) 851-857
22. Park J.I., Strock C.J., Ball D.W., and Nelkin B.D. The Ras/Raf/MEK/extracellular signal-regulated kinase pathway induces autocrine-paracrine growth inhibition via the leukemia inhibitory factor/JAK/STAT pathway. Mol. Cell. Biol. 23 (2003) 543-554
23. Frederick A., Rolfe M., and Chiu M.I. The human UNP locus at 3p21.31 encodes two tissue-selective, cytoplasmic isoforms with deubiquitinating activity that have reduced expression in small cell lung carcinoma cell lines. Oncogene 16 (1998) 153-165