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2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) (2016)
Shenzhen, China
Dec. 15, 2016 to Dec. 18, 2016
ISBN: 978-1-5090-1612-9
pp: 455-459
Xinliang Zhu , Department of Computer Science and Engineering, The University of Texas at Arlington, USA
Jiawen Yao , Department of Computer Science and Engineering, The University of Texas at Arlington, USA
Guanghua Xiao , Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, USA
Yang Xie , Department of Clinical Science, The University of Texas Southwestern Medical Center, Dallas, USA
Jaime Rodriguez-Canales , Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA
Edwin R. Parra , Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA
Carmen Behrens , Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA
Ignacio I. Wistuba , Department of Translational Molecular Pathology, The University of Texas MD Anderson Cancer Center, Houston, USA
Junzhou Huang , Department of Computer Science and Engineering, The University of Texas at Arlington, USA
ABSTRACT
Imaging-genetic data mapping is important for clinical outcome prediction like survival analysis. In this paper, we propose a supervised conditional Gaussian graphical model (SuperCGGM) to uncover survival associated mapping between pathological images and genetic data. The proposed method integrates heterogeneous modal data into the survival model by weighted projection within the data. To obtain a sparse solution, we employ l-1 regularization to the partial log likelihood loss function and propose a cyclic coordinate ascent algorithm to solve it. It also gives a way to bridge the gap between the supervised model with conditional Gaussian graphical model (CGGM). Compared to nine state-of-the-art methods like SuperPCA, CGGM, etc., our method is superior due to its ability of integrating diverse information from heterogeneous modal data in a supervised way. The extensive experiments also show the strong power of SuperCGGM in mapping survival associated image and gene expression signatures.
INDEX TERMS
Data models, Correlation, Cancer, Gene expression, Lungs, Predictive models, Feature extraction
CITATION

X. Zhu et al., "Imaging-genetic data mapping for clinical outcome prediction via supervised conditional Gaussian graphical model," 2016 IEEE International Conference on Bioinformatics and Biomedicine (BIBM), Shenzhen, China, 2016, pp. 455-459.
doi:10.1109/BIBM.2016.7822559
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