A clustering based hybrid system for mass spectrometry data analysis

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 5265 LNBI, Issue , 2008, Pages 98-109

  Yang, Pengyi ^a   Zhang, Zili ^a,b  

^a SOUTHWEST UNIVERSITY   (China)

^b DEAKIN UNIVERSITY   (Australia)

Author keywords

[No Author keywords available]

Indexed keywords

BIOINFORMATICS; BIOMARKERS; CLUSTER ANALYSIS; COMPUTER AIDED DIAGNOSIS; FEATURE EXTRACTION; GENETIC ALGORITHMS; HYBRID SYSTEMS; MASS SPECTROMETRY; REDUNDANCY;

BIOLOGICAL INFORMATION; BIOMARKER IDENTIFICATION; DISEASE CLASSIFICATION; FEATURE EXTRACTION AND SELECTION; HIGH THROUGHPUT EXPERIMENTS; K-MEAN CLUSTERING ALGORITHM; MASS SPECTROMETRY DATA ANALYSIS; MULTI-OBJECTIVE GENETIC ALGORITHM;

K-MEANS CLUSTERING;

EID: 57049137817     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/978-3-540-88436-1_9     Document Type: Conference Paper

References (26)

1. Morris, J.S., Coombes, K.R., Koomen, J., Baggerly, K.A., Kobayashi, R.: Feature extraction and quantification for mass spectrometry in biomedical applications using the mean spectrum. Bioinformatics 21(9), 1764-1775 (2005)
2. Petricoin, E.F., Liotta, L.A.: SELDI-TOF-based serum proteomic pattern diagnostics for early detection of cencer. Curr. Opin. Biotechnol. 15, 24-30 (2004)
3. Petricoin, E.F., Ornstein, D.K., Paweletz, C.P., Ardekani, A.M., Hackett, P.S., Hitt, B.A., Velassco, A., Trueco, C., Wiegand, L., Wood, K., Simone, C.B., Levine, P.J., Linehan, W.M., Emmert-Buck, M.R., Steinberg, S.M., Kohn, E.G., Liotta, L.A.: Serum Proteomic Patterns for Detection of Prostate Cancer. Journal of the National Cancer Institute 94(20), 1576-1578 (2002)
4. Petricoin, E.F., Ardekani, A.M., Hitt, B.A., Levine, P.J., Fusaro, V.A., SteinBerg, S.M., Mills, G.B., Simone, C., Fishman, D.A., Kohn, E.C., Liotta, L.A.: Use of proteomic patterns in serum to identify ovarian cancer. The Lancet 359, 572-577 (2002)
5. Li, L., Umbach, D.M., Terry, P., Taylor, J.A.: Application of the GA/KNN method to SELDI proteomics data. Bioinformatics 20(10), 1638-1640 (2004)
6. Yu, J.S., Ongarello, S., Fiedler, R., Chen, X.W., Toffolo, G., Cobelli, C., Trajanoski, Z.: Ovarian cancer identification based on dimensionality reduction for high-throughput mass spectrometry data. Bioinformatics 21(10), 2200-2209 (2005)
7. Boguski, M.S., McIntosh, M.W.: Biomedical informatics for proteomics. Nature 422, 233-236 (2003)
8. Somorjai, R.L., Dolenko, B., Baumgartner, R.: Class prediction and discovery using gene microarray and protenomics mass spectroscopy data: curses, caveats, cautions. Bioinformatics 19(12), 1484-1491 (2003)
9. Ding, C., Peng, H.: Minimum Redundancy Feature Selection From Microarray Gene Expression Data. Journal of Bioinformatics and Computational Biology 3(2), 185-205 (2005)
10. Golub, T.R,., Tamayo, D.K., Tamayo, P., Huard, C., Gaasenbeek, M., Mesirov, J.P., Coller, H., Loh, M.L., Downing, J.R., Caligiuri, M.A., Boomfield, C.D., Lander, E.S.: Molecular Classification of Cancer: Class Discovery and Class Prediction by Gene Expression Monitoring. Science 286, 531-537 (1999)
11. Liu, H., Li, J., Wang, L.: A Comparative Study on Feature Selection and Classification Methods Using Gene Expression Profiles and Proteomic Patterns. Genome Informatics 13, 51-60 (2002)
12. Su, Y., Murali, T., Pavlovic, V., Schaffer, M., Kasif, S.: RankGene: Identification of Diagnostic Genes Based on Expression Data. Bioinformatics 19(12), 1578-1579 (2003)
13. Kohavi, R., John, G.: Wrapper for feature subset selection. Artificial Intelligence 97(1-2), 273-324 (1997)
14. Jaeger, J., Sengupta, R., Ruzzo, W.L.: Improved Gene Selection for Clssification of Microarrays. Pac. Symp. Biocomput., 53-64 (2003)
15. Jirapech-Umpai, T., Aitken, S.: Feature Selection and Classification for Microarray Data Analysis: Evolutionary Methods for Identifying Predictive Genes. BMC Bioinformatics 6, 146 (2005)
16. Yang, P.Y., Zhang, Z.L.: Hybrid Methods to Select Informative Gene Sets in Microarray Data Classification. In: Orgun, M.A., Thornton, J. (eds.) AI 2007. LNCS (LNAI), vol. 4830, pp. 811-815. Springer, Heidelberg (2007)
17. Yang, P.Y., Zhang, Z.L.: A Hybrid Approach to Selecting Susceptible Single Nucleotide Polymorphisms for Complex Disease Analysis. In: Proceedings of BMEI 2008, pp. 214-218. IEEE, Los Alamitos (2008)
18. Quinlan, J.R.: Learning efficient classification procedures and their applicaiton to chess and games. In: Machine Learning: An Artificial Intelligence Approach. Morgan Kaufmann, San Mateo (1983)
19. Quinlan, J.R,.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Mateo (1993)
20. Blum, A.L., Langley, P.: Selection of relevent Features and Examples in Machine Learning. Artificial Intelligence 97(1-2), 245-271 (1997)
21. Geurts, P., Fillet, M., de Seny, D., Meuwis, M.A., Malaise, M., Merville, M.P., Wehenkel, L.: Proteomic mass spectra, classifcation using decision tree based ensemble methods. Bioinformatics 21, 3138-3145 (2005)
22. Wang, Y., Makedon, F., Ford, J., Pearlman, J.: HykGene: A Hybrid Approach for Selecting Marker Genes for Phenotype Classification using Microarray Gene Expression Data. Bioinformatics 21(8), 1530-1537 (2005)
23. Zhang, Z.L., Yang, P.Y.: An Ensemble of Classifier with Genetic Algorithm Based Feature Selection, (accepted by IEEE Intelligent Informatics Bulletin)
24. Cai, Z., Goebel, R., Salavatipour, M.R., Lin, G.: Selecting Dissimilar Genes for Multi-Class Classification, an Application in Cancer Subtyping. BMC Bioinformatics 8, 206 (2007)
25. Hanczar, B., Courtine, M., Benis, A., Hennegar, C., Clement, K., Zucker, J.-D.: Improving classification of microarray data using prototype-based feature selection. SIGKDD Explorations 5, 23-30 (2003)
26. Saeys, Y., Inza, I., Larrañaga, P.: A review of feature selection techniques in bioinformatics. Bioinformatics 23(19), 2507-2517 (2007)