जीन अभिव्यक्ति डेटा Archives — MATLAB Number ONE

Metasample Based Sparse Representation for Tumor Classification

global MATLAB — Mon, 07 May 2018 05:13:21 +0000

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 […]

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Molecular Pattern Discovery based on Penalized Matrix Decomposition

global MATLAB — Thu, 19 Apr 2018 13:29:54 +0000

In this project, we proposed to use the penalized matrix decomposition (PMD) to extract metasamples from gene expression data. With the sparsity constrain on the decomposition factors, the extracted metasamples can well capture the intrinsic structures of the samples in the same class. Meanwhile, the PMD factors of each sample are good indicators of the class label of it. […]

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Tumor Clustering Using Non-negative Matrix Factorization with Gene Selection

global MATLAB — Mon, 16 Apr 2018 06:48:50 +0000

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 […]

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