Imaging-genetic data mapping for clinical outcome prediction via supervised conditional Gaussian graphical model

Xinliang Zhu, Jiawen Yao, Guanghua Xiao, Yang Xie, Jaime Rodriguez-Canales, Edwin R. Parra, Carmen Behrens, Ignacio I. Wistuba, Junzhou Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

4 Scopus citations

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.

Original languageEnglish (US)
Title of host publicationProceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages455-459
Number of pages5
ISBN (Electronic)9781509016105
DOIs
Publication statusPublished - Jan 17 2017
Event2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 - Shenzhen, China
Duration: Dec 15 2016Dec 18 2016

Other

Other2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016
CountryChina
CityShenzhen
Period12/15/1612/18/16

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Keywords

  • CGGM
  • Data mapping
  • Imaging-genetic
  • Survival analysis

ASJC Scopus subject areas

  • Genetics
  • Medicine (miscellaneous)
  • Genetics(clinical)
  • Biochemistry, medical
  • Biochemistry
  • Molecular Medicine
  • Health Informatics

Cite this

Zhu, X., Yao, J., Xiao, G., Xie, Y., Rodriguez-Canales, J., Parra, E. R., ... Huang, J. (2017). Imaging-genetic data mapping for clinical outcome prediction via supervised conditional Gaussian graphical model. In Proceedings - 2016 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2016 (pp. 455-459). [7822559] Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/BIBM.2016.7822559