A Novel Approach for Biclustering Gene Expression Data Using Modular Singular Value Decomposition

机译：利用模块化奇异值分解对基因表达数据进行聚类的新方法

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Clustering is a method of unsupervised learning, and a common technique for statistical data analysis used in many fields, including machine learning, data mining, pattern recognition, image analysis and bioinformatics. Recently, biclustering (or co-clustering), performing simultaneous clustering on the row and column dimensions of the data matrix, has been shown to be remarkably effective in a variety of applications. In this paper we propose a novel approach to biclustering gene expression data based on Modular Singular Value Decomposition (Mod-SVD). Instead of applying SVD directly on on data matrix, the proposed approach computes SVD on modular fashion. Experiments conducted on synthetic and real dataset demonstrated the effectiveness of the algorithm in gene expression data.

机译：聚类是一种无监督学习的方法，是统计数据分析的一种常用技术，广泛用于机器学习，数据挖掘，模式识别，图像分析和生物信息学等领域。最近，在数据矩阵的行和列维度上同时进行聚类的双聚类（或双聚类）已被证明在多种应用中非常有效。在本文中，我们提出了一种基于模块化奇异值分解（Mod-SVD）的基因表达数据聚类的新方法。所提出的方法不是将SVD直接应用于数据矩阵，而是以模块化方式计算SVD。在合成数据集和真实数据集上进行的实验证明了该算法在基因表达数据中的有效性。

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来源
《Computational intelligence methods for bioinformatics and biostatistics》|2009年|p.254-265|共12页
会议地点 Genoa(IT);Genoa(IT)
作者
V.N. Manjunath Aradhya; Francesco Masulli; Stefano Rovetta;
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作者单位

Dept. of Computer and Information Sciences University of Genova, Via Dodecaneso 35, 16146 Genova, Italy,Dept. of ISE, Dayananda Sagar College of Engg, Bangalore, India - 560078;

Dept. of Computer and Information Sciences University of Genova, Via Dodecaneso 35, 16146 Genova, Italy,Sbarro Institute for Cancer Research and Molecular Medicine, Center for Biotechnology, Temple University, BioLife Science Bldg., 1900 N 12th Street Philadelphia, PA 19122 USA;

Dept. of Computer and Information Sciences University of Genova, Via Dodecaneso 35, 16146 Genova, Italy;

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原文格式 PDF
正文语种 eng
中图分类生物工程学（生物技术）;人工智能理论;
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入库时间 2022-08-26 14:04:25

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1. Biclustering of gene expression data using biclustering iterative signature algorithm and biclustering coherent column [J] . E. Saravana Kumar, K. Vengatesan, R.P. Singh, International journal of biomedical engineering and technology . 2018,第3a4期

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2. Gene Expression Data Analysis Using a Novel Approach to Biclustering Combining Discrete and Continuous Data [J] . Christinat Y., Wachmann B., Lei Zhang IEEE/ACM transactions on computational biology and bioinformatics . 2008,第4期

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3. Biclustering using Parallel Fuzzy Approach for Analysis of Microarray Gene Expression Data [J] . Dwitiya Tyagi-Tiwari, Manoj Jha, Namita Srivastava, International Journal of Computer Science and Security . 2015,第5期

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4. A Novel Approach for Biclustering Gene Expression Data Using Modular Singular Value Decomposition [C] . V. N. Manjunath Aradhya, Francesco Masulli, Stefano Rovetta International Meeting on Computational Intelligence Methods for Bioinformatics and Biostatistics . 2010

机译：使用模块化奇异值分解的一种新方法，用于使用模块化奇异值分解的基因表达数据
5. Tunable Biclustering Algorithm for Analyzing Large Gene Expression Data Sets [D] . Singh, Amartya. 2019

机译：用于分析大型基因表达数据集的可调谐双板算法
6. Identification of nutrient partitioning genes participating in rice grain filling by singular value decomposition (SVD) of genome expression data [O] . Abraham Anderson, Matthew Hudson, Wenqiong Chen, 2003

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7. Identification of nutrient partitioning genes participating in rice grain filling by singular value decomposition (SVD) of genome expression data [O] . 2003

机译：通过基因组表达数据的奇异值分解（SVD）鉴定参与水稻籽粒灌浆的营养分配基因

A Novel Approach for Biclustering Gene Expression Data Using Modular Singular Value Decomposition

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