Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives
نویسندگان
چکیده
منابع مشابه
Clustering Time-Series Gene Expression Data Using Smoothing Spline Derivatives
Microarray data acquired during time-course experiments allow the temporal variations in gene expression to be monitored. An original postprandial fasting experiment was conducted in the mouse and the expression of 200 genes was monitored with a dedicated macroarray at 11 time points between 0 and 72 hours of fasting. The aim of this study was to provide a relevant clustering of gene expression...
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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 ...
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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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Data clustering techniques have been applied to extract information from gene expression data for two decades. A large volume of novel clustering algorithms have been developed and achieved great success. However, due to the diverse structures and intensive noise, there is no reliable clustering approach can be applied to all gene expression data. In this paper, we aim to the feature of high no...
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ژورنال
عنوان ژورنال: EURASIP Journal on Bioinformatics and Systems Biology
سال: 2007
ISSN: 1687-4145
DOI: 10.1155/2007/70561