检索结果-内蒙古大学图书馆

IEEE TRANSACTIONS ON NANOBIOSCIENCE 2011年第4期10卷 225-238页

作者： Istepanian, Robert S. H. Sungoor, Ala Nebel, Jean-Christophe Kingston Univ London Mobile Informat & Network Technol Res Ctr Kingston Upon Thames KT1 2EE Surrey England Kingston Univ London Fac Sci Engn & Comp Kingston Upon Thames KT1 2EE Surrey England

Genomic signal processing is a new area of research that combines advanced digital signal processing methodologies for enhanced genetic data analysis. It has many promising applications in bioinformatics and next generation of healthcare systems, in particular, in the field of microarray data clustering. In this paper we present a comparative performance analysis of enhanced digital spectral analysis methods for robust clustering of gene expression across multiple microarray data samples. Three digital signal processing methods: linear predictive coding, wavelet decomposition, and fractal dimension are studied to provide a comparative evaluation of the clustering performance of these methods on several microarray datasets. The results of this study show that the fractal approach provides the best clustering accuracy compared to other digital signal processing and well known statistical methods.

关键词： Discrete wavelet fractal dimension genomic signal processing linear predictive coding microarray clustering vector quantization

来源：评论

学校读者我要写书评

暂无评论

Min-max kurtosis stratum mean: An improved K-means cluster initialization approach for microarray gene clustering on multidimensional big data

引用

CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2022年第23期34卷 e7185-e7185页

作者： Pandey, Kamlesh Kumar Shukla, Diwakar Dr Hari Singh Gour Vishwavidyalaya Dept Comp Sci & Applicat Sagar Madhya Pradesh India

microarray gene clustering is a big data application that employs the K-means (KM) clustering algorithm to identify hidden patterns, evolutionary relationships, unknown functions and gene trends for disease diagnosis, tissue detection and biological analysis. The selection of initial centroids is a major issue in the KM algorithm because it influences the effectiveness, efficiency and local optima of the cluster. The existing initial centroid initialization algorithm is computationally expensive and degrades cluster quality due to the large dimensionality and interconnectedness of microarray gene data. To deal with this issue, this study proposed the min-max kurtosis stratum mean (MKSM) algorithm for big data clustering in a single machine environment. The MKSM algorithm uses kurtosis for dimension selection, mean distance for gene relationship identification, and stratification for heterogeneous centroid extraction. The results of the presented algorithm are compared to the state-of-the-art initialization strategy on twelve microarray gene datasets utilizing internal, external and statistical assessment criteria. The experimental results demonstrate that the MKSMKM algorithm reduces iterations, distance computation, data comparison and local optima, and improves cluster performance, effectiveness and efficiency with stable convergence.

关键词： big data clustering gene clustering initial centroid K-means microarray clustering multidimensional clustering

来源：评论

学校读者我要写书评

暂无评论

Common Subcluster Mining to Explore Molecular Markers of Lung Cancer 1st

Common Subcluster Mining to Explore Molecular Markers of Lun...

引用

1st International Conference on Intelligent Computing and Communication, (ICIC2)

作者： Sadhu, Arnab Bhattacharyya, Balaram Visva Bharati Univ Dept Comp & Syst Sci Santini Ketan 731235 W Bengal India

ISBN: (纸本)9789811020353;9789811020346

We present a methodology, common subcluster mining, to explore gene expression data for possible biomarkers of lung cancer. Subclusters refer to the peaks formed through superimposition of clusters obtained from expression data of normal samples. Application of the method on the corresponding data sets from diseased samples extracts the genes that undergo high fold changes. The potential candidate genes are examined on the datasets of Stage I through stage IV of the disease. Few genes emerge as indicative molecular markers of lung cancer.

关键词： Common subcluster mining Lung cancer biomarker microarray clustering Differential gene expression Molecular marker

来源：评论

学校读者我要写书评

暂无评论

Discovery of cancer linked biomarker genes through common subcluster mining

Discovery of cancer linked biomarker genes through common su...

引用

International Conference on Bioinformatics and Systems Biology (BSB)

作者： Sadhu, Arnab Bhattacharyya, Balaram Visva Bharati Univ Dept Comp & Syst Sci Santini Ketan W Bengal India

ISBN: (纸本)9781509022618

Gene expression data from microarray experiments offer enormous scope for exploring the genetic relationship of deadly diseases. The motivation is to explore possible molecular biomarkers of such diseases with a view to early and periodic detection. A study has been reported in this paper with a methodology for common subcluster mining using FCM clustering. Subcluster refers to the peak formed through superimposition of clusters obtained from expressional data, both from the normal and diseased samples separately. Experiments are carried out on datasets of lung cancer, Acute Myeloid Leukemia(AML) and breast cancer employing the algorithm for common subcluster mining. Results are found to match to a large extent with those obtained in previous studies. Few genes emerge as indicative molecular biomarkers of respective diseases.

关键词： common subcluster mining Acute Myeloid Leukemia breast cancer lung cancer microarray clustering differential gene expression molecular marker

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：