Lasso logistic regression based approach for extracting plants coregenes responding to abiotic stresses

Proceedings of 4th International Workshop on Advanced Computational Intelligence, IWACI 2011

Volumn , Issue , 2011, Pages 461-464

  Liu, Jinxing ^a,b   Zheng, Chunhou ^b   Xu, Yong ^a  

^a HARBIN INSTITUTE OF TECHNOLOGY   (China)

^b QUFU NORMAL UNIVERSITY   (China)

Author keywords

[No Author keywords available]

Indexed keywords

ABIOTIC STRESS; ABSOLUTE VALUES; GENE EXPRESSION DATA; LOGISTIC REGRESSIONS; NONZERO ENTRIES; REGRESSION COEFFICIENT; SPARSE METHODS;

ARTIFICIAL INTELLIGENCE; GENE EXPRESSION; LUNAR SURFACE ANALYSIS; REGRESSION ANALYSIS;

LOGISTICS;

EID: 84863386440     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IWACI.2011.6160051     Document Type: Conference Paper

References (14)

1. G.J. Allen, S.P. Chu, K. Schumacher, C.T. Shimazaki, D. Vafeados, A. Kemper, et al., "Alteration of stimulus-specific guard cell calcium oscillations and stomatal closing in the Arabidopsis det3 mutant," Science, vol. 289, no. 5488, pp. 2338-2342, Sep. 2000.
2. J. Kilian, D. Whitehead, J. Horak, D. Wanke, S. Weinl, O. Batistic, et al., "The AtGenExpress global stress expression data set: protocols, evaluation and model data analysis of UV-B light, drought and cold stress responses," Plant J, vol. 50, no.2, pp.347-363, Apr. 2007. (Pubitemid 46624141)
3. R. Luss and A. d'Aspremont, "Clustering and feature selection using sparse principal component analysis," Optimization and Engineering, vol. 11, no. 1, pp. 145-157, DOI: 10.1007/s11081-008-9057-z, 2010.
4. D.M. Witten, R. Tibshirani and T. Hastie, "A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis," Biostatistics, vol. 10, no. 3, pp. 515-534, Apr. 2009.
5. C.H. Zheng, L. Zhang, T.Y. Ng, C.K. Shiu and S.L. Wang, "Inferring the Transcriptional Modules using Penalized Matrix Decomposition," in ICIC 2010, LNAI, Springer-Verlag Berlin, Heidelberg, vol. 6216, pp. 35-41, 2010.
6. J. Zhu, "Classification of gene microarrays by penalized logistic regression," Biostatistics, vol. 5, no. 3, pp. 427-443, 2004. (Pubitemid 41180217)
7. Y. Xu, Q. Zhu, J. Wang, "Breast cancer diagnosis based on a kernel orthogonal transform," Neural Computing and Applications, Feb. 2011. [Online]. Avaliable: http://www.springerlink.com/content/0ju76h6716306mt1.
8. R.E. Fan, K.W. Chang, C.J. Hsieh, .XR. Wang and C.J. Lin, "LIBLINEAR: A Library for Large Linear Classification," Journal of Machine Learning Research, vol. 9, pp. 1871-1874, 2008.
9. G.X. Yuan, K.W. Chang, C.J. Hsieh and C.J. Lin, "A comparison of optimization methods and software for large-scale L1-regualrized linear classification," Journal of Machine Learning Research, vol. 11, pp. 3183-3234, Nov. 2010.
10. M. Garcia-Magariños, A. Antoniadis, R. Cao and W. González- Manteiga, "Lasso logistic regression, Gsoft and the cyclic coordinate descent algorithm: Application to gene expression data, Statistical Applications in Genetics and Molecular Biology," Statistical Applications in Genetics and Molecular Biology, vol. 9, no. 1, article 30, Aug. 2010.
11. Affymetrix Team, NASCArrays, [Online]. [http://affy.arabidopsis.info/ narrays/experimentbrowse.pl], Accessed Dec. 1, 2010.
12. Z. Wu, R.A. Irizarryy, R. Gentlemanz, F. Martinez-Murillo and F. Spencer, "A model based background adjustment for oligonucleotide expression arrays," J Am Stat Assoc, vol. 99, no. 468, pp. 909-917, Dec. 2004. (Pubitemid 39665934)
13. M. Journee, Y. Nesterov, P. Richtarik and R. Sepulchre, "Generalized power Method for sparse principal component analysis," The Journal of Machine Learning Research, vol. 11, pp. 1-30, Feb. 2010.
14. M. Ashburner, C.A. Ball, J.A. Blake, D. Botstein, H. Butler, J.M. Cherry, et al., "The Gene Ontology Consortium. Gene ontology: tool for the unification of biology," Nat Genet, vol. 25, no.1, pp. 25-29, May 2000. (Pubitemid 30257031)