Genome-Wide Association Studies of Copy Number Variation in Glioblastoma

Xiong, Momiao; Dong, Hua; Siu, Hoicheong; Peng, Gang; Wang, Yi; Jin, Li

doi:10.1109/icbbe.2010.5516437

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…The second variant is rs2818384, which is a synonymous mutation in the gene JAKMIP3, and has a cis association with the methylation marker cg05652533. Copy-number variants in JAKMIP3 have been previously associated with glioblastoma (Xiong et al, 2010). The final variant found to influence AMAR is rs42663, which is a missense mutation in the gene GTPBP10, and associates with cg27367526 in the gene STEAP2.…”

Section: Methylome Aging Rate and Its Associationsmentioning

confidence: 99%

Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

et al. 2013

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Summary The ability to measure human aging from molecular profiles has practical implications in many fields, including disease prevention and treatment, forensics, and extension of life. Although chronological age has been linked to changes in DNA methylation, the methylome has not yet been used to measure and compare human aging rates. Here, we build a quantitative model of aging using measurements at more than 450,000 CpG markers from the whole blood of 656 human individuals, aged 19 to 101. This model measures the rate at which an individual’s methylome ages, which we show is impacted by gender and genetic variants. Furthermore, we show that differences in aging rates help explain epigenetic drift and are reflected in the transcriptome. Our model highlights specific components of the aging process and provides a quantitative read-out for studying the role of methylation in age-related disease.

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Section: Methylome Aging Rate and Its Associationsmentioning

confidence: 99%

Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

et al. 2013

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Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

et al. 2019

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Background: Understanding the complex biological mechanisms of cancer patient survival using genomic and clinical data is vital, not only to develop new treatments for patients, but also to improve survival prediction. However, highly nonlinear and high-dimension, low-sample size (HDLSS) data cause computational challenges to applying conventional survival analysis. Results: We propose a novel biologically interpretable pathway-based sparse deep neural network, named Cox-PASNet, which integrates high-dimensional gene expression data and clinical data on a simple neural network architecture for survival analysis. Cox-PASNet is biologically interpretable where nodes in the neural network correspond to biological genes and pathways, while capturing the nonlinear and hierarchical effects of biological pathways associated with cancer patient survival. We also propose a heuristic optimization solution to train Cox-PASNet with HDLSS data. Cox-PASNet was intensively evaluated by comparing the predictive performance of current state-of-the-art methods on glioblastoma multiforme (GBM) and ovarian serous cystadenocarcinoma (OV) cancer. In the experiments, Cox-PASNet showed out-performance, compared to the benchmarking methods. Moreover, the neural network architecture of Cox-PASNet was biologically interpreted, and several significant prognostic factors of genes and biological pathways were identified. Conclusions: Cox-PASNet models biological mechanisms in the neural network by incorporating biological pathway databases and sparse coding. The neural network of Cox-PASNet can identify nonlinear and hierarchical associations of genomic and clinical data to cancer patient survival. The open-source code of Cox-PASNet in PyTorch implemented for training, evaluation, and model interpretation is available at: https://github.com/DataX-JieHao/Cox-PASNet.

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Genome-Wide Association Studies of Copy Number Variation in Glioblastoma

Cited by 2 publications

References 5 publications

Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

Genome-wide Methylation Profiles Reveal Quantitative Views of Human Aging Rates

Interpretable deep neural network for cancer survival analysis by integrating genomic and clinical data

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