A Fuzzy Approach For Clustering Gene Expression Time Series Data
نویسندگان
چکیده
منابع مشابه
A Fuzzy Approach for Clustering Gene Expression Time Series Data
Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8...
متن کاملA new approach for clustering gene expression time series data
Identifying groups of genes that manifest similar expression patterns is crucial in the analysis of gene expression time series data. Choosing a similarity measure to determine the similarity or distance between profiles is an important task. This paper proposes a suitable dissimilarity measure for gene expression time series data sets. It also presents a graph-based clustering method for findi...
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With rapid development in information gathering technologies and access to large amounts of data, we always require methods for data analyzing and extracting useful information from large raw dataset and data mining is an important method for solving this problem. Clustering analysis as the most commonly used function of data mining, has attracted many researchers in computer science. Because o...
متن کاملClustering Gene Expression Time Series Data
Efficiently and effectively finding the genes with similar behaviors from microarray data is an important task in bioinformatics community. Co-expression genes have the same behavior or are controlled by the same regulatory mechanisms. Clustering analysis is a very popular technique to group the co-expressed genes into the same cluster. One of the key issues for clustering gene expression time ...
متن کاملClustering short time series gene expression data
MOTIVATION Time series expression experiments are used to study a wide range of biological systems. More than 80% of all time series expression datasets are short (8 time points or fewer). These datasets present unique challenges. On account of the large number of genes profiled (often tens of thousands) and the small number of time points many patterns are expected to arise at random. Most clu...
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ژورنال
عنوان ژورنال: International Journal of Computer Science and Information Technology
سال: 2011
ISSN: 0975-4660
DOI: 10.5121/ijcsit.2011.3415