Kohonen’s self-organizing maps as applied to graphical visualization of some yeast DNA data – Supplement: PLOTS
نویسندگان
چکیده
Figure 1 Spider plot for the yeast gene YBL008w (HIR1); position 1 the walker visits every first nucleotide of codons and moves a unit up if the nucleotide is G, down if it is C, right if it is A and left if it is T; position 2 the walker visits every second nucleotide of codons, and proceeds the same way; position 3 the walker visits every third nucleotide of codons, and proceeds the same way . {paa.jpg}
منابع مشابه
Kohonen’s self-organizing maps as applied to graphical visualization of some yeast DNA data
We analyze a set of data describing N=3300 yeast genes, each gene characterized by d=13 variables (traits). First we performed an explorative data analysis and stated a high multivariate kurtosis. Next we clustered the data and visualized them using Kohonen’s self-organizing maps. This permitted us to get an idea how the data are distributed in the multivariate space.
متن کاملAir Quality Modelling by Kohonen’s Self-organizing Feature Maps and LVQ Neural Networks
The paper presents a design of parameters for air quality modelling and the classification of districts into classes according to their pollution. Further, it presents a model design, data pre-processing, the designs of various structures of Kohonen’s Self-organizing Feature Maps (unsupervised methods), the clustering by K-means algorithm and the classification by Learning Vector Quantization n...
متن کاملKSOM and MLP neural networks for on-line estimating the efficiency of an activated sludge process
This work is devoted to the study of the Saint Cyprien (south of France) activated sludge WWTP process and to the on-line estimation of chemical parameters (influent and effluent chemical oxygen demand, ammonia and suspended solids) not easily measurable on-line. Their knowledge makes it possible to estimate the process efficiency and to provide reliable information for the plant monitoring. A ...
متن کاملAnalysis 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...
متن کاملNeural and Statistical Methods for the Visualization of Multidimensional Data
In many fields of engineering science we have to deal with multivariate numerical data. In order to choose the technique that is best suited to a given task, it is necessary to get an insight into the data and to “understand” them. Much information allowing the understanding of multivariate data, that is the description of its global structure, the presence and shape of clusters or outliers, ca...
متن کامل