Computer Methods and Programs in Biomedicine

읽은 날	2020.08.12	학술지	ELSEVIER
제목	Computer Methods and Programs in Biomedicine
저자	Gang Li a , b , ∗, Depeng Han a , Chao Wang a , Wenxing Hu c , Vince D. Calhoun d , Yu-Ping Wang c
한줄요약	조현병 classification을 위한 새로운 ML model인 DCCSAE의 개발과 그 효용성
초록	Background and objective: 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 informa- tion 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. Methods: 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 canon- ically 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. Results: The proposed deep canonically correlated sparse autoencoder can not only use complex nonlin- ear transformation and dimension reduction, but also achieve more accurate classifications. Our experi- ments showed the proposed method achieved an accuracy of 95.65% for SNP data sets and an accuracy of 80.53% for fMRI data sets. Conclusions: Experiments demonstrated higher accuracy of using the proposed method over other con- ventional models when classifying schizophrenia patients and healthy controls.
키워드	Imaging-genetic associations, Canonical correlation analysis, Sparse autoencoder, Deep canonically correlated sparse autoencoder, Schizophrenia classification
의의	정확도가 높으면서 demension reduction의 이득을 취한 모델의 개발
비판점