Identifying biomarkers for acupuncture treatment via an optimization model

2011 IEEE International Conference on Systems Biology, ISB 2011

Volumn , Issue , 2011, Pages 319-326

  Wang, Yong ^a   Wu, Qiao Feng ^b   Chen, Chen ^a   Yan, Xian Zhong ^c   Yu, Shu Guang ^b   Zhang, Xiang Sun ^a   Liang, Fan Rong ^b  

^a CHINESE ACADEMY OF SCIENCES   (China)

^b CHENGDU UNIVERSITY OF TRADITIONAL CHINESE MEDICINE   (China)

^c NATIONAL CENTER OF BIOMEDICAL ANALYSIS   (China)

Author keywords

[No Author keywords available]

Indexed keywords

CANCER GENOMICS; HIGH-DIMENSIONAL; LEAVE-ONE-OUT CROSS VALIDATIONS; MERIDIAN POINTS; METABOLIC DATA; METABOLIC PROFILES; METABOLIC PROFILING; MOLECULAR LEVELS; MOLECULAR MECHANISM; OPTIMIZATION MODELS;

METABOLITES;

METABOLISM;

EID: 80054860474     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ISB.2011.6033172     Document Type: Conference Paper

References (13)

1. M. T. Cabỳoglu, N. Ergene, and U. Tan. The mechanism of acupuncture and clinical applications, International journal of neuroscience, Vol. 116 No. 2, 2006.
2. L. Chen, R. S. Wang, and X. S. Zhang. Biomolecular Networks: Methods and Applications in Systems Biology. John Wiley & Sons, Hoboken, New Jersey. July, 2009.
3. Y. Wang, T. Joshi, X. S. Zhang, D. Xu, and L. Chen. Inferring gene regulatory networks from multiple microarray datasets. Bioinformatics, 2006, 22:2413-2420.
4. Qiaofeng Wu, Shizhen Xu, et al., Metabonomics and pattern recognition study on the specificity of foot-yangming Meridian points, Shanghai J Acu-mox, Vol. 29 No. 9, 2010.
5. Ilya Levner. Feature selection and nearest centroid classification for protein mass spectrometry BMC Bioinformatics 2005, 6:68 doi:10.1186/1471-2105-6- 68
6. Hastie T, Tibshirani R, Friedman J: The Elements of Statistical Learning. Springer Series in Statistics. Springer Verlag, New York; 2001.
7. Park H, Jeon M, Rosen JB: Lower dimensional representation of text data based on centroids and least squares. BIT 2003, 43(2):1-22.
8. Levner I: Proteomic pattern recognition. Technical report, University of Alberta, No: TR04-10 2004.
9. Daniela Witten and Robert Tibshirani. A comparison of fold-change and the t-statistic for microarray data analysis. Technical report, Stanford university. 2007.
10. B. Krishnapuram, L. Carin, and A. Hartemink. Joint Classifier and Feature Optimization for Comprehensive Cancer Diagnosis Using Gene Expression Data. Journal of Computational Biology, 11, pp. 227C242, 2004.
11. B. Krishnapuram, M. Figueiredo, L. Carin, and A. Hartemink. Sparse Multinomial Logistic Regression: Fast Algorithms and Generalization Bounds. IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), 27, June 2005. pp. 957C968, 2005.
12. P. Pratapa, E. Patz, and A. Hartemink. Finding Diagnostic Biomarkers in Proteomic Spectra. In Pacific Symposium on Biocomputing 2006(PSB06), World Scientific: New Jersey. pp. 279C290, 2006.
13. G. S. Eichler, S. Huang, D. E. Ingber, Gene Expression Dynamics Inspector (GEDI): for integrative analysis of expression profiles, Bioinformatics, 19(17), 2321-2, 2003.