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Imaging-genetic data mapping for clinical outcome prediction via supervised conditional Gaussian graphical model | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Imaging-genetic data mapping is important for clinical outcome prediction like survival analysis. In this paper, we propose a supervised conditional Gaussian graphical mo...Show More

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.
Date of Conference: 15-18 December 2016
Date Added to IEEE Xplore: 19 January 2017
ISBN Information:
Conference Location: Shenzhen, China

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