Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer

BMC Bioinformatics

Research Article

Le Ou-Yang¹  Hong Yan²  Dao-Qing Dai³  Yuan Zhu⁴  Xiao-Fei Zhang⁵  Meng-Yun Wu⁶ 

[1] College of Information Engineering, Shenzhen University, Nanhai Avenue, 518060, Shenzhen, China;Department of Electronic and Engineering, City University of Hong Kong, Tat Chee Avenue, 999077, Hong Kong, China;Intelligent Data Center and Department of Mathematics, Sun Yat-Sen University, Xingang West Road, 510275, Guangzhou, China;School of Automation, China University of Geosciences, Lumo Road, 430074, Wuhan, China;School of Mathematics and Statistics & Hubei Key Laboratory of Mathematical Sciences, Central China Normal University, Luoyu Road, 430079, Wuhan, China;School of Statistics and Management, Shanghai University of Finance and Economics, Guoding Road, 200433, Shanghai, China;Key Laboratory of Mathematical Economics SUFE, Ministry of Education, Guoding Road, 200433, Shanghai, China;

关键词: Protein-protein interaction network;    Edge-biomarker discovery;    Network-based pairwise interaction;    Node degree;    Adaptive elastic net;   

DOI  :  10.1186/s12859-016-0951-7

 received in 2015-08-06, accepted in 2016-01-28,  发布年份 2016

来源: Springer

【 摘 要 】

BackgroudTo 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.ResultsIn 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.ConclusionsThe 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.

【 授权许可】

CC BY   
© Wu et al. 2016

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