@inproceedings{c4fecffa17c44520abf737caa6b2b8db,
title = "Cancer subtype identification using deep learning approach",
abstract = "In this paper, a framework using deep learning approach is proposed to identify two subtypes of human colorectal carcinoma cancer. The identification process uses information from gene expression and clinical data which is obtained from data integration process. One of deep learning architecture, multimodal Deep Boltzmann Machines (DBM) is used for data integration process. The joint representation gene expression and clinical is later used as Restricted Boltzmann Machines (RBM) input for cancer subtype identification. Kaplan Meier survival analysis is employed to evaluate the identification result. The curves on survival plot obtained from Kaplan Meier analysis are tested using three statistic tests to ensure that there is a significant difference between those curves. According to Log Rank, Generalized Wilcoxon and Tarone-Ware, the two groups of patients with different cancer subtypes identified using the proposed framework are significantly different.",
keywords = "RBM, cancer subtype, deep learning, multimodal DBM",
author = "Syafiandini, {Arida Ferti} and Ito Wasito and Setiadi Yazid and Aries Fitriawan and Mukhlis Amien",
note = "Publisher Copyright: {\textcopyright} 2016 IEEE.; 2016 International Conference on Computer, Control, Informatics and its Applications, IC3INA 2016 ; Conference date: 03-10-2016 Through 05-10-2016",
year = "2017",
month = feb,
day = "23",
doi = "10.1109/IC3INA.2016.7863033",
language = "English",
series = "Proceeding - 2016 International Conference on Computer, Control, Informatics and its Applications: Recent Progress in Computer, Control, and Informatics for Data Science, IC3INA 2016",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "108--112",
booktitle = "Proceeding - 2016 International Conference on Computer, Control, Informatics and its Applications",
address = "United States",
}