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	<title>بيانات التعبير الجيني Archives &#8212; MATLAB Number ONE</title>
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		<title>Metasample Based Sparse Representation for Tumor Classification</title>
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		<pubDate>Mon, 07 May 2018 05:13:21 +0000</pubDate>
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					<description><![CDATA[<p>A reliable and accurate identification of the type of tumors is crucial to the proper treatment of cancers. In recent years, it has been shown that sparse representation (SR) by l1 -norm minimization is robust to noise, outliers and even incomplete measurements, and SR has been successfully used for classification. This paper presents a new SR based method [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/metasample-based-sparse-representation-for-tumor-classification/">Metasample Based Sparse Representation for Tumor Classification</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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		<title>Tumor Clustering Using Non-negative Matrix Factorization with Gene Selection</title>
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		<pubDate>Mon, 16 Apr 2018 06:48:50 +0000</pubDate>
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					<description><![CDATA[<p>&#160; In this project, we employed ICA to model the gene expression data for gene selection, and then applied NMF and its extensions, i.e., SNMF and NMFSC to cancer clustering using the selected genes. The proposed method was validated on the leukemia dataset, embryonal tumors dataset from the central nervous system, and the medulloblastoma dataset. It can be found that improved [&#8230;]</p>
<p>The post <a href="https://matlab1.com/shop/matlab-code/tumor-clustering-using-non-negative-matrix-factorization-with-gene-selection/">Tumor Clustering Using Non-negative Matrix Factorization with Gene Selection</a> appeared first on <a href="https://matlab1.com">MATLAB Number ONE</a>.</p>
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