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      Integrating Imaging Genomic Data in the Quest for Biomarkers of Schizophrenia Disease

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          Abstract

          <p class="first" id="P1">It’s increasingly important but difficult to determine potential biomarkers of schizophrenia disease, owing to the complex pathophysiology of this disease. In this study, a network-fusion based framework was proposed to identify genetic biomarkers of complex diseases. Genomic, epigenomic and neuroimaging data were integrated by network fusion. A three-step feature selection was applied to single nucleotide polymorphisms (SNPs), DNA methylation and functional magnetic resonance imaging (fMRI) data to select Important features, which were then used to construct two gene networks in different states for the SNPs and DNA methylation data, respectively. Two health networks (one is for SNP data and the other is for DNA methylation data) were combined into one health network from which health minimum spanning trees (MSTs) were extracted. And two disease networks were also the same. Those genes with significant changes were determined as SCZ biomarkers by comparing MSTs in two different states and they were finally validated from five aspects. The effectiveness of the proposed discovery framework was also demonstrated by comparing with other network-based discovery methods. In summary, our approach provides a general framework for discovering gene biomarkers of the complex diseases, which can be applied to the diagnosis of the complex diseases in future. </p>

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            Shortest Connection Networks And Some Generalizations

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              A penalized matrix decomposition, with applications to sparse principal components and canonical correlation analysis.

              We present a penalized matrix decomposition (PMD), a new framework for computing a rank-K approximation for a matrix. We approximate the matrix X as circumflexX = sigma(k=1)(K) d(k)u(k)v(k)(T), where d(k), u(k), and v(k) minimize the squared Frobenius norm of X - circumflexX, subject to penalties on u(k) and v(k). This results in a regularized version of the singular value decomposition. Of particular interest is the use of L(1)-penalties on u(k) and v(k), which yields a decomposition of X using sparse vectors. We show that when the PMD is applied using an L(1)-penalty on v(k) but not on u(k), a method for sparse principal components results. In fact, this yields an efficient algorithm for the "SCoTLASS" proposal (Jolliffe and others 2003) for obtaining sparse principal components. This method is demonstrated on a publicly available gene expression data set. We also establish connections between the SCoTLASS method for sparse principal component analysis and the method of Zou and others (2006). In addition, we show that when the PMD is applied to a cross-products matrix, it results in a method for penalized canonical correlation analysis (CCA). We apply this penalized CCA method to simulated data and to a genomic data set consisting of gene expression and DNA copy number measurements on the same set of samples.
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                Author and article information

                Journal
                IEEE/ACM Transactions on Computational Biology and Bioinformatics
                IEEE/ACM Trans. Comput. Biol. and Bioinf.
                Institute of Electrical and Electronics Engineers (IEEE)
                1545-5963
                1557-9964
                2374-0043
                September 1 2018
                September 1 2018
                : 15
                : 5
                : 1480-1491
                Article
                10.1109/TCBB.2017.2748944
                6207076
                28880187
                © 2018

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