title: Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer.
abstract: BACKGROUND
  To facilitate advances in personalized medicine, it is important to detect predictive, stable and interpretable biomarkers related with different clinical characteristics. These clinical characteristics may be heterogeneous with respect to underlying interactions between genes. Usually, traditional methods just focus on detection of differentially expressed genes without taking the interactions between genes into account. Moreover, due to the typical low reproducibility of the selected biomarkers, it is difficult to give a clear biological interpretation for a specific disease. Therefore, it is necessary to design a robust biomarker identification method that can predict disease-associated interactions with high reproducibility.
  
  
  RESULTS
  In this article, we propose a regularized logistic regression model. Different from previous methods which focus on individual genes or modules, our model takes gene pairs, which are connected in a protein-protein interaction network, into account. A line graph is constructed to represent the adjacencies between pairwise interactions. Based on this line graph, we incorporate the degree information in the model via an adaptive elastic net, which makes our model less dependent on the expression data. Experimental results on six publicly available breast cancer datasets show that our method can not only achieve competitive performance in classification, but also retain great stability in variable selection. Therefore, our model is able to identify the diagnostic and prognostic biomarkers in a more robust way. Moreover, most of the biomarkers discovered by our model have been verified in biochemical or biomedical researches.
  
  
  CONCLUSIONS
  The proposed method shows promise in the diagnosis of disease pathogenesis with different clinical characteristics. These advances lead to more accurate and stable biomarker discovery, which can monitor the functional changes that are perturbed by diseases. Based on these predictions, researchers may be able to provide suggestions for new therapeutic approaches.
authors.affiliation: School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.|Key Laboratory of Mathematical Economics SUFE, Ministry of Education, Guoding Road, Shanghai, 200433, China. wu.mengyun@mail.shufe.edu.cn.
authors.forename: Meng-Yun
authors.initials: MY
authors.lastname: Wu
authors.affiliation: School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, Wuhan, 430079, China. zhangxf@mail.ccnu.edu.cn.
authors.forename: Xiao-Fei
authors.initials: XF
authors.lastname: Zhang
authors.affiliation: Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, Guangzhou, 510275, China. stsddq@mail.sysu.edu.cn.
authors.forename: Dao-Qing
authors.initials: DQ
authors.lastname: Dai
authors.affiliation: College of Information Engineering, Shenzhen University, Nanhai Avenue, Shenzhen, 518060, China. ouyangle@mail2.sysu.edu.cn.
authors.forename: Le
authors.initials: L
authors.lastname: Ou-Yang
authors.affiliation: School of Automation, China University of Geosciences, Lumo Road, Wuhan, 430074, China. zhuyuan7@mail2.sysu.edu.cn.
authors.forename: Yuan
authors.initials: Y
authors.lastname: Zhu
authors.affiliation: Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, Hong Kong, 999077, China. h.yan@cityu.edu.hk.
authors.forename: Hong
authors.initials: H
authors.lastname: Yan
mesh_terms: D015415:Biomarkers / Q000378:metabolism*; D001943:Breast Neoplasms / Q000175:diagnosis* / Q000235:genetics*; D005260:Female; D006801:Humans; D016015:Logistic Models; D008962:Models, Theoretical; D057285:Precision Medicine; D060066:Protein Interaction Maps; D015203:Reproducibility of Results
chemical_list: D015415:Biomarkers