A temporal precedence based clustering method for gene expression microarray data
نویسندگان
چکیده
منابع مشابه
A spectral clustering method for microarray data
This paper considers a clustering method motivated by a multivariate analysis of variance model and computationally based on eigenanalysis (thus the term “spectral” in the title). Our focus is on large problems, and we present the method in the context of clustering genes using microarray expression data. We provide an e5cient computational algorithm and discuss its properties and interpretatio...
متن کاملClustering Algorithms for Time Series Gene Expression in Microarray Data
illustrations, 75 numbered references. Clustering techniques are important for gene expression data analysis. However, efficient computational algorithms for clustering time-series data are still lacking. This work documents two improvements on an existing profile-based greedy algorithm for short time-series data; the first one is implementation of a scaling method on the pre-processing of the ...
متن کاملComparative Study of Clustering Techniques for Gene Expression Microarray Data
Scientists can now monitor on a genomic scale the patterns of gene expression under varying environmental conditions. With this rapidly growing wealth of information comes the need for organizing and analyzing the data. One natural approach is to group together genes with similar patterns of expression. Several approaches have suggested various alternatives for similarity metrics and clustering...
متن کاملMemory-Efficient Clustering Algorithms for Microarray Gene Expression Data
1 Human Genome Center, Institute of Medical Science, University of Tokyo, 4-6-1 Shirokanedai, Minato-ku, Tokyo 108-8639, Japan 2 Department of Informatics, Graduate School of Information Science and Electrical Engineering, Kyushu University, 6-10-1 Hakozaki, Higashi-ku, Fukuoka 812-8581, Japan 3 Bioinformatics Center, Institute for Chemical Research, Kyoto University, Uji, Kyoto 6110011, Japan ...
متن کاملGene Identification from Microarray Data for Diagnosis of Acute Myeloid and Lymphoblastic Leukemia Using a Sparse Gene Selection Method
Background: Microarray experiments can simultaneously determine the expression of thousands of genes. Identification of potential genes from microarray data for diagnosis of cancer is important. This study aimed to identify genes for the diagnosis of acute myeloid and lymphoblastic leukemia using a sparse feature selection method. Materials and Methods: In this descriptive study, the expressio...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: BMC Bioinformatics
سال: 2010
ISSN: 1471-2105
DOI: 10.1186/1471-2105-11-68