@inproceedings{fe0bafa2b84745fd8197b9c1c3b944ca,
title = "Extraction of co-expressed discriminative features of schizophrenia in imaging epigenetics framework",
abstract = "Integration of imaging and non-imaging data has been a heated topic in biomedicine. While functional magnetic resonance imaging (fMRI) can serve as endo-phenotype for mental disorders, many recent researches have confirmed the essential role played by epigenetic factors in the progress of various mental diseases including Schizophrenia(SZ), which fosters an emerging branch imaging epigenetics. In this study, we focus on the integration of fMRI and DNA methylation to have a deeper understanding of SZ: we applied a model combining Lasso with Canonical Correlation Analysis (CCA) for joint DNA methylation and fMRI analysis of 184 subjects (80 patients,104 healthy controls). In the model, the regression term focuses on extracting the discriminative features associated with the disease, while the CCA term incorporates the co-expression among extracted features.",
keywords = "collaborative learning, feature selection, imaging epigenetics, multi-task learning, schizophrenia",
author = "Yuntong Bai and Zille Pascal and Calhoun, {Vince D.} and Wang, {Yu Ping}",
note = "Publisher Copyright: {\textcopyright} 2019 SPIE.; Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging ; Conference date: 19-02-2019 Through 21-02-2019",
year = "2019",
doi = "10.1117/12.2513024",
language = "English (US)",
series = "Progress in Biomedical Optics and Imaging - Proceedings of SPIE",
publisher = "SPIE",
editor = "Barjor Gimi and Andrzej Krol",
booktitle = "Medical Imaging 2019",
}