[1]

Golub TR, Slonim DK, Tamayo P, Huard C, Gaasenbeek M, et al. 1999. Molecular classification of cancer: class discovery and class prediction by gene expression monitoring. Science 286:531−37

doi: 10.1126/science.286.5439.531
[2]

Alon U, Barkai N, Notterman DA, Gish K, Ybarra S, et al. 1999. Broad patterns of gene expression revealed by clustering analysis of tumor and normal colon tissues probed by oligonucleotide arrays. Proceedings of the National Academy of Sciences of the United States of America 96:6745−50

doi: 10.1073/pnas.96.12.6745
[3]

Brown MP, Grundy WN, Lin D, Cristianini N, Sugnet CW, et al. 2000. Knowledge-based analysis of microarray gene expression data by using support vector machines. Proceedings of the National Academy of Sciences of the United States of America 97:262−67

doi: 10.1073/pnas.97.1.262
[4]

Furey TS, Cristianini N, Duffy N, Bednarski DW, Schummer M, et al. 2000. Support vector machine classification and validation of cancer tissue samples using microarray expression data. Bioinformatics 16:906−14

doi: 10.1093/bioinformatics/16.10.906
[5]

Guyon I, Weston J, Barnhill S, Vapnik V. 2002. Gene selection for cancer classification using support vector machines. Machine learning 46:389−422

doi: 10.1023/A:1012487302797
[6]

Zhu J, Rosset S, Hastie T, Tibshirani R, Zhu J, et al. 2003. 1-norm support vector machines. Proceedings of the 17 th International Conference on Neural Information Processing Systems, 9−11 December 2003, Whistler, British Columbia, Canada. Cambridge, MA, United States: MIT Press. pp. 49−56. https://dl.acm.org/doi/10.5555/2981345.2981352

[7]

Tibshirani R. 1996. Regression shrinkage and selection via the lasso. Journal of the Royal Statistical Society: Series B (Methodological) 58:267−88

doi: 10.1111/j.2517-6161.1996.tb02080.x
[8]

Wang L, Zhu J, Zou H. 2006. The doubly regularized support vector machine. Statistica Sinica 16:589−615

[9]

Zou H, Hastie T. 2005. Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society Series B: Statistical Methodology 67:301−20

doi: 10.1111/j.1467-9868.2005.00503.x
[10]

Liang Y, Liu C, Luan XZ, Leung KS, Chan TM, et al. 2013. Sparse logistic regression with a L1/2 penalty for gene selection in cancer classification. BMC Bioinformatics 14:198

doi: 10.1186/1471-2105-14-198
[11]

Zhang HH, Ahn J, Lin X, Park C. 2006. Gene selection using support vector machines with non-convex penalty. Bioinformatics 22:88−95

doi: 10.1093/bioinformatics/bti736
[12]

Shen X, Tseng GC, Zhang X, Wong WH. 2003. On ψ-learning. Journal of the American Statistical Association 98:724−34

doi: 10.1198/016214503000000639
[13]

Collobert R, Sinz F, Weston J, Bottou L. 2006. Trading convexity for scalability. Proceedings of the 23 rd international conference on Machine learning, 25−29 June 2006, Pittsburgh, Pennsylvania, USA. New York, USA: ACM. pp. 201−8. doi: 10.1145/1143844.1143870

[14]

Wu Y, Liu Y. 2007. Robust truncated hinge loss support vector machines. Journal of the American Statistical Association 102:974−83

doi: 10.1198/016214507000000617
[15]

Mason L, Baxter J, Bartlett P, Frean M. 1999. Boosting algorithms as gradient descent. Advances in neural information processing systems. Cambridge, MA, United States: MIT Press. pp. 512−18. https://dl.acm.org/doi/10.5555/3009657.3009730

[16]

Bartlett PL, Jordan MI, McAuliffe JD. 2006. Convexity, classification, and risk bounds. Journal of the American Statistical Association 101:138−56

doi: 10.1198/016214505000000907
[17]

Zhou X, Tuck DP. 2007. MSVM-RFE: extensions of SVM-RFE for multiclass gene selection on DNA microarray data. Bioinformatics 23:1106−14

doi: 10.1093/bioinformatics/btm036
[18]

Fan J, Li R. 2001. Variable selection via nonconcave penalized likelihood and its oracle properties. Journal of the American Statistical Association 96:1348−60

doi: 10.1198/016214501753382273
[19]

Thi Hoai An L, Dinh Tao P. 1997. Solving a class of linearly constrained indefinite quadratic problems by DC algorithms. Journal of global optimization 11:253−85

doi: 10.1023/A:1008288411710
[20]

Yuille AL, Rangarajan A. 2003. The concave-convex procedure. Neural computation 15:915−36

doi: 10.1162/08997660360581958
[21]

Friedman J, Hastie T, Tibshirani R. 2010. Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software 33:1−22

[22]

Yang Y, Zou H. 2013. An efficient algorithm for computing the HHSVM and its generalizations. Journal of Computational and Graphical Statistics 22:396−415

doi: 10.1080/10618600.2012.680324
[23]

Gong P, Zhang C, Lu Z, Huang J, Ye J. 2013. A general iterative shrinkage and thresholding algorithm for non-convex regularized optimization problems. In international conference on machine learning 28:37−45

[24]

Boyd S, Vandenberghe L. 2004. Convex optimization. Cambridge, UK: Cambridge University Press. doi: 10.1017/cbo9780511804441

[25]

Wang L, Zhu J, Zou H. 2008. Hybrid huberized support vector machines for microarray classification and gene selection. Bioinformatics 24:412−19

doi: 10.1093/bioinformatics/btm579
[26]

Hastie T, Tibshirani R, Friedman J. 2009. The elements of statistical learning: data mining, inference, and prediction. New York, NY: Springer. doi: 10.1007/978-0-387-84858-7

[27]

Dudoit S, Fridlyand J, Speed TP. 2002. Comparison of discrimination methods for the classification of tumors using gene expression data. Journal of the American Statistical Association 97:77−87

doi: 10.1198/016214502753479248
[28]

Li L, Weinberg CR, Darden TA, Pedersen LG. 2001. Gene selection for sample classification based on gene expression data: study of sensitivity to choice of parameters of the GA/KNN method. Bioinformatics 17:1131−42

doi: 10.1093/bioinformatics/17.12.1131
[29]

Ambroise C, McLachlan GJ. 2002. Selection bias in gene extraction on the basis of microarray gene-expression data. Proceedings of the National Academy of Sciences of the United States of America 99:6562−66

doi: 10.1073/pnas.102102699
[30]

Betapudi V, Licate LS, Egelhoff TT. 2006. Distinct roles of nonmuscle myosin II isoforms in the regulation of MDA-MB-231 breast cancer cell spreading and migration. Cancer Research 66:4725−33

doi: 10.1158/0008-5472.CAN-05-4236
[31]

Fujiya M, Konishi H, Mohamed Kamel MK, Ueno N, Inaba Y, et al. 2014. microRNA-18a induces apoptosis in colon cancer cells via the autophagolysosomal degradation of oncogenic heterogeneous nuclear ribonucleoprotein A1. Oncogene 33:4847−56

doi: 10.1038/onc.2013.429
[32]

Shailubhai K, Yu HH, Karunanandaa K, Wang JY, Eber SL, et al. 2000. Uroguanylin treatment suppresses polyp formation in the Apc(Min/+) mouse and induces apoptosis in human colon adenocarcinoma cells via cyclic GMP. Cancer Research 60:5151−57

[33]

Dong L, Wang F, Yin X, Chen L, Li G, et al. 2014. Overexpression of S100P promotes colorectal cancer metastasis and decreases chemosensitivity to 5-FU in vitro. Molecular and cellular biochemistry 389:257−64

doi: 10.1007/s11010-013-1947-5