Hindawi Computational and Mathematical Methods in Medicine Volume 2019, Article ID 8418760, 10 pages https://doi.org/10.1155/2019/8418760 Research Article Integrative Deep Learning for Identifying Differentially Expressed (DE) Biomarkers Jayeon Lim ,1 SoYoun Bang,2 Jiyeon Kim,3 Cheolyong Park ,3 JunSang Cho,4 and SungHwan Kim 1 1Department of Applied Statistics, Konkuk University, Seoul, Republic of Korea 2Department of Data Science, Konkuk University, Seoul, Republic of Korea 3Department of Statistics, Keimyung University, Daegu, Republic of Korea 4Industry-University Cooperation Foundation, Konkuk University, Seoul, Republic of Korea Correspondence should be addressed to SungHwan Kim;
[email protected] Received 5 April 2019; Revised 19 June 2019; Accepted 4 August 2019; Published 2 November 2019 Academic Editor: Esmaeil Ebrahimie Copyright © 2019 Jayeon Lim et al. +is is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. As a large amount of genetic data are accumulated, an effective analytical method and a significant interpretation are required. Recently, various methods of machine learning have emerged to process genetic data. In addition, machine learning analysis tools using statistical models have been proposed. In this study, we propose adding an integrated layer to the deep learning structure, which would enable the effective analysis of genetic data and the discovery of significant biomarkers of diseases. We conducted a simulation study in order to compare the proposed method with metalogistic regression and meta-SVM methods. +e objective function with lasso penalty is used for parameter estimation, and the Youden J index is used for model comparison.