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Neuroimage. 2014 Nov 15;102 Pt 1:192-206. doi: 10.1016/j.neuroimage.2013.08.015. Epub 2013 Aug 27.

Bi-level multi-source learning for heterogeneous block-wise missing data.

Author information

1
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA.
2
Huawei Noah's Ark Lab, Hong Kong.
3
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA.
4
Imaging Genetics Center, Laboratory of Neuro Imaging, Department of Neurology & Psychiatry, UCLA School of Medicine, Los Angeles, CA, USA.
5
School of Computing, Informatics, and Decision Systems Engineering, Arizona State University, Tempe, AZ, USA; Center for Evolutionary Medicine and Informatics, The Biodesign Institute, Arizona State University, Tempe, AZ, USA. Electronic address: jieping.ye@asu.edu.

Abstract

Bio-imaging technologies allow scientists to collect large amounts of high-dimensional data from multiple heterogeneous sources for many biomedical applications. In the study of Alzheimer's Disease (AD), neuroimaging data, gene/protein expression data, etc., are often analyzed together to improve predictive power. Joint learning from multiple complementary data sources is advantageous, but feature-pruning and data source selection are critical to learn interpretable models from high-dimensional data. Often, the data collected has block-wise missing entries. In the Alzheimer's Disease Neuroimaging Initiative (ADNI), most subjects have MRI and genetic information, but only half have cerebrospinal fluid (CSF) measures, a different half has FDG-PET; only some have proteomic data. Here we propose how to effectively integrate information from multiple heterogeneous data sources when data is block-wise missing. We present a unified "bi-level" learning model for complete multi-source data, and extend it to incomplete data. Our major contributions are: (1) our proposed models unify feature-level and source-level analysis, including several existing feature learning approaches as special cases; (2) the model for incomplete data avoids imputing missing data and offers superior performance; it generalizes to other applications with block-wise missing data sources; (3) we present efficient optimization algorithms for modeling complete and incomplete data. We comprehensively evaluate the proposed models including all ADNI subjects with at least one of four data types at baseline: MRI, FDG-PET, CSF and proteomics. Our proposed models compare favorably with existing approaches.

KEYWORDS:

Alzheimer's disease; Block-wise missing data; Multi-modal fusion; Multi-source; Optimization

PMID:
23988272
PMCID:
PMC3937297
DOI:
10.1016/j.neuroimage.2013.08.015
[Indexed for MEDLINE]
Free PMC Article
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