自编码
典型相关
人工智能
精神分裂症(面向对象编程)
模式识别(心理学)
相关性
计算机科学
功能磁共振成像
转化(遗传学)
神经影像学
磁共振成像
深度学习
数学
医学
心理学
精神科
神经科学
生物
遗传学
放射科
基因
程序设计语言
几何学
作者
Gang� Li,De-Peng Han,Chao Wang,Wei Hu,Vince D. Calhoun,Yu-Ping Wang
标识
DOI:10.1016/j.cmpb.2019.105073
摘要
Imaging genetics has been widely used to help diagnose and treat mental illness, e.g., schizophrenia, by combining magnetic resonance imaging of the brain and genomic information for comprehensive and systematic analysis. As a result, utilizing the correlation between magnetic resonance imaging of the brain and genomic information is becoming an important challenge. In this paper, the joint analysis of single nucleotide polymorphisms and functional magnetic resonance imaging is conducted for comprehensive study of schizophrenia. We developed a deep canonically correlated sparse autoencoder to classify schizophrenia patients from healthy controls, which can address the limitation of many existing methods such as canonical correlation analysis, deep canonical correlation analysis and sparse autoencoder. The proposed deep canonically correlated sparse autoencoder can not only use complex nonlinear transformation and dimension reduction, but also achieve more accurate classifications. Our experiments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. Experiments demonstrated higher accuracy of using the proposed method over other conventional models when classifying schizophrenia patients and healthy controls.
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