Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning

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Please use this identifier to cite or link to this item: https://irlib.pccu.edu.tw/handle/987654321/48859

Title:	Overall survival prediction of non-small cell lung cancer by integrating microarray and clinical data with deep learning
Authors:	Lai, YH (Lai, Yu-Heng) Chen, WN (Chen, Wei-Ning) Hsu, TC (Hsu, Te-Cheng) Lin, C (Lin, Che) Tsao, Y (Tsao, Yu) Wu, SM (Wu, Semon)
Contributors:	化學系
Keywords:	EXPRESSION ADENOCARCINOMA PROGNOSIS CHEMOTHERAPY ONCOGENES PROTEINS GENE HUR
Date:	2020-12-01
Issue Date:	2020-12-10 13:43:25 (UTC+8)
Abstract:	Non-small cell lung cancer (NSCLC) is one of the most common lung cancers worldwide. Accurate prognostic stratification of NSCLC can become an important clinical reference when designing therapeutic strategies for cancer patients. With this clinical application in mind, we developed a deep neural network (DNN) combining heterogeneous data sources of gene expression and clinical data to accurately predict the overall survival of NSCLC patients. Based on microarray data from a cohort set (614 patients), seven well-known NSCLC biomarkers were used to group patients into biomarker- and biomarker+ subgroups. Then, by using a systems biology approach, prognosis relevance values (PRV) were then calculated to select eight additional novel prognostic gene biomarkers. Finally, the combined 15 biomarkers along with clinical data were then used to develop an integrative DNN via bimodal learning to predict the 5-year survival status of NSCLC patients with tremendously high accuracy (AUC: 0.8163, accuracy: 75.44%). Using the capability of deep learning, we believe that our prediction can be a promising index that helps oncologists and physicians develop personalized therapy and build the foundation of precision medicine in the future.
Relation:	SCIENTIFIC REPORTS 卷冊: 10 期: 1 文獻號碼: 4679
Appears in Collections:	[Department of Chemistry & Graduate Institute of Applied Chemistry ] journal articles

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