Analysis of gene expression data using self-organizing maps
نویسندگان
چکیده
منابع مشابه
Analysis of gene expression data using self-organizing maps.
DNA microarray technologies together with rapidly increasing genomic sequence information is leading to an explosion in available gene expression data. Currently there is a great need for efficient methods to analyze and visualize these massive data sets. A self-organizing map (SOM) is an unsupervised neural network learning algorithm which has been successfully used for the analysis and organi...
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The microarray technology allows researchers to simultaneously measure gene expression levels of thousands of genes. Analysis of data produced by such experiments provides knowledge about the gene function. An important step in the analysis of gene expression data is the detection of genes with similar expression patterns. Real-time computational tools for organization and visualization are cru...
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شبکه خود سازمانده پرکاربردترین شبکه عصبی برای انجام خوشه بندی و کوانتیزه نمودن برداری است. از زمان معرفی این شبکه تاکنون، از این روش در مسائل مختلف در حوزه های گوناگون استفاده و توسعه ها و بهبودهای متعددی برای آن ارائه شده است. شبکه خودسازمانده از تعدادی سلول برای تخمین تابع توزیع الگوهای ورودی در فضای چندبعدی استفاده می کند. احتمال وجود سلول مرده مشکلی اساسی در الگوریتم شبکه خودسازمانده به حسا...
Analysis and visualization of gene expression data using Self-Organizing Maps
Cluster structure of gene expression data obtained from DNA microarrays is analyzed and visualized with the Self-Organizing Map (SOM) algorithm. The SOM forms a non-linear mapping of the data to a two-dimensional map grid that can be used as an exploratory data analysis tool for generating hypotheses on the relationships, and ultimately of the function of the genes. Similarity relationships wit...
متن کاملDouble self-organizing maps to cluster gene expression data
Clustering is a very useful and important technique for analyzing gene expression data. Self-organizing map (SOM) is one of the most useful clustering algorithms. SOM requires the number of clusters to be one of the initialization parameters prior to clustering. However, this information is unavailable in most cases, particularly in gene expression data. Thus, the validation results from SOM ar...
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ژورنال
عنوان ژورنال: FEBS Letters
سال: 1999
ISSN: 0014-5793
DOI: 10.1016/s0014-5793(99)00524-4