Consensus Clustering for Time Course Gene Expression Microarray Data
نویسندگان
چکیده
منابع مشابه
Clustering of Time-Course Gene Expression Data
Microarray experiments have been used to measure genes’ expression levels under different cellular conditions or along certain time course. Initial attempts to interpret these data begin with grouping genes according to similarity in their expression profiles. The widely adopted clustering techniques for gene expression data include hierarchical clustering, self-organizing maps, and K-means clu...
متن کاملConsensus clustering of gene expression microarray data using genetic algorithms
This work presents a new consensus clustering method for gene expression microarray data based on a genetic algorithm. Using two datasets – DA and DB – as input, the genetic algorithm examines putative partitions for the samples in DA, selecting biomarkers that support such partitions. The biomarkers are then used to build a classifier which is used in DB to determine its samples classes. The g...
متن کاملEntropy-based Consensus for Distributed Data Clustering
The increasingly larger scale of available data and the more restrictive concerns on their privacy are some of the challenging aspects of data mining today. In this paper, Entropy-based Consensus on Cluster Centers (EC3) is introduced for clustering in distributed systems with a consideration for confidentiality of data; i.e. it is the negotiations among local cluster centers that are used in t...
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 کاملJackknife distances for clustering time–course gene expression data
Clustering time–course gene expression data is a common tool to find co–regulated genes and groups of genes with similar temporal or spatial expression patterns. The distance measure used for clustering has major impact on the properties of the resulting clusters. As technical problems can easily distort the microarray data there is a need for distance measures which are able to deal with outli...
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: Communications for Statistical Applications and Methods
سال: 2005
ISSN: 2287-7843
DOI: 10.5351/ckss.2005.12.2.335