title: JOINT IDENTIFICATION OF IMAGING AND PROTEOMICS BIOMARKERS OF ALZHEIMER'S DISEASE USING NETWORK-GUIDED SPARSE LEARNING.
abstract: Identification of biomarkers for early detection of Alzheimer's disease (AD) is an important research topic. Prior work has shown that multimodal imaging and biomarker data could provide complementary information for prediction of cognitive or AD status. However, the relationship among multiple data modalities are often ignored or oversimplified in prior studies. To address this issue, we propose a network-guided sparse learning model to embrace the complementary information and inter-relationships between modalities. We apply this model to predict cognitive outcome from imaging and proteomic data, and show that the proposed model not only outperforms traditional ones, but also yields stable multimodal biomarkers across cross-validation trials.
authors.affiliation: Radiology and Imaging Sciences, BioHealth Informatics, Indiana University, IN, USA.
authors.forename: Jingwen
authors.initials: J
authors.lastname: Yan
authors.affiliation: Computer Science and Engineering, University of Texas at Arlington, TX, USA.
authors.forename: Heng
authors.initials: H
authors.lastname: Huang
authors.affiliation: Radiology and Imaging Sciences, BioHealth Informatics, Indiana University, IN, USA.
authors.forename: Sungeun
authors.initials: S
authors.lastname: Kim
authors.affiliation: Genetics, Community and Family Medicine, School of Medicine at Dartmouth College, NH, USA.
authors.forename: Jason
authors.initials: J
authors.lastname: Moore
authors.affiliation: Radiology and Imaging Sciences, BioHealth Informatics, Indiana University, IN, USA.
authors.forename: Andrew
authors.initials: A
authors.lastname: Saykin
authors.affiliation: Radiology and Imaging Sciences, BioHealth Informatics, Indiana University, IN, USA.
authors.forename: Li
authors.initials: L
authors.lastname: Shen
keywords: Sparse learning; cognitive outcome; neuroimaging; proteomic biomarker; regression