Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model
被引:12
|
作者:
Zhou, Zhiming
论文数: 0引用数: 0
h-index: 0
机构:
Macau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R ChinaMacau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
Zhou, Zhiming
[1
]
Huang, Haihui
论文数: 0引用数: 0
h-index: 0
机构:
Macau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
Shaoguan Univ, Shaoguan, Guangdong, Peoples R ChinaMacau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
Huang, Haihui
[1
,2
]
Liang, Yong
论文数: 0引用数: 0
h-index: 0
机构:
Macau Univ Sci & Technol, Macau Inst Syst Engn, Macau, Peoples R China
Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau, Peoples R ChinaMacau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
Liang, Yong
[3
,4
]
机构:
[1] Macau Univ Sci & Technol, Fac Informat Technol, Macau, Peoples R China
[2] Shaoguan Univ, Shaoguan, Guangdong, Peoples R China
[3] Macau Univ Sci & Technol, Macau Inst Syst Engn, Macau, Peoples R China
[4] Macau Univ Sci & Technol, Collaborat Lab Intelligent Sci & Syst, Macau, Peoples R China
BACKGROUND: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection. OBJECTIVE: The aim of this paper is to give the model efficient gene selection capability. METHODS: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification. RESULTS: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods. CONCLUSIONS: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.
机构:
Shanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China
Minist Educ, Key Lab Math Econ SUFE, Guoding Rd, Shanghai 200433, Peoples R ChinaShanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China
Wu, Meng-Yun
Zhang, Xiao-Fei
论文数: 0引用数: 0
h-index: 0
机构:
Cent China Normal Univ, Sch Math & Stat, Luoyu Rd, Wuhan 430079, Peoples R China
Cent China Normal Univ, Hubei Key Lab Math Sci, Luoyu Rd, Wuhan 430079, Peoples R ChinaShanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China
Zhang, Xiao-Fei
Dai, Dao-Qing
论文数: 0引用数: 0
h-index: 0
机构:
Sun Yat Sen Univ, Intelligent Data Ctr, Xingang West Rd, Guangzhou 510275, Guangdong, Peoples R China
Sun Yat Sen Univ, Dept Math, Xingang West Rd, Guangzhou 510275, Guangdong, Peoples R ChinaShanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China
Dai, Dao-Qing
论文数: 引用数:
h-index:
机构:
Ou-Yang, Le
Zhu, Yuan
论文数: 0引用数: 0
h-index: 0
机构:
China Univ Geosci, Sch Automat, Lumo Rd, Wuhan 430074, Peoples R ChinaShanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China
Zhu, Yuan
Yan, Hong
论文数: 0引用数: 0
h-index: 0
机构:
City Univ Hong Kong, Dept Elect & Engn, Tat Chee Ave, Hong Kong 999077, Hong Kong, Peoples R ChinaShanghai Univ Finance & Econ, Sch Stat & Management, Guoding Rd, Shanghai 200433, Peoples R China