T or onen, P., Kolehmainen, M., Wong, G., Castren, E., 1999. Analysis of gene expression data using self-organizing maps. Federation of European Biochemical Societies Letters 451, 142-14.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....data, or gene expression pro les, have been proposed. Eisen et al. 8] applied the hierarchical clustering [25] which have been a widely used tool [2] 14] 16] 22] It has some variants such as ipping the internal nodes [3] in the tree and using neural networks [12] Self organizing maps [29][31] and k means algorithm [30] have also been used for the same purpose. Ben Dor et al. 4] developed an algorithm, Cluster anity search technique (CAST) which has a good theoretical basis. Merz and Zell [18] proposed a memetic algorithm for the problem formulated as nding the minimum ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Letters, 451:142-146, 1999. 17

....data, then, can be analyzed by biologists. A number of algorithms for clustering gene expression pro les were proposed. Eisen et al. 10] applied hierarchical clustering [38] which has been a widely used tool [1] 22] 24] 35] It also has some variants [2] 17] Self organizing maps (SOMs) 42][44] and k means clustering [43] were also used for the same purpose. Ben Dor et al. 3] developed an algorithm, cluster anity search technique (CAST) which has a good theoretical basis. Merz and Zell [28] proposed a memetic algorithm for the problem formulated as nding the minimum sum of squares ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Letters, 451:142-146, 1999.

....[27] This data is replete with undiscovered biological knowledge which holds the promise of revolutionising biotechnology and medicine. KDD techniques are well suited to extracting this knowledge. Currently most KDD analysis of bioinformatic data has been based on using unsupervised methods e.g. [9, 17, 32], but some has been based on supervised methods [4, 7, 14] New KDD methods are constantly required to meet the new challenges presented by new forms of bioinformatic data. Perhaps the least analysed form of genomics data is that from phenotype experiments [25, 22, 18] In these experiments ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Lett., 451(2):142--6, May 1999.

....resulting clusters 20 are visualized by presenting for each cluster its average expression pattern with error bars. Clusters are presented in their grid order, as clusters of close nodes tend to be similar. Another implementation of SOM for clustering gene expression pro les was developed by [Toronen et al. 1999]. 11.5 Assessment of solutions A key question in the design and analysis of clustering techniques is how to evaluate solutions. We present in this section gures of merit for measuring the quality of a clustering solution. Di erent measures are applicable in di erent situations, depending on ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Letters, 451:142-146, 1999.

....least squares; Yeast; Modeling 3 With the advent of microarray technology, simultaneous expression levels of thousands of genes can be monitored. Several researchers have proposed new and existing clustering algorithms in conjunction with microarray data sets to classify genes in various groups [2,3,4,5]. See [6] for a minireview. All these techniques are based, in one way or the other, on the similarity of the temporal expression patterns of various genes. Whereas these analyses often help the scientist in identifying genes with similar biological functions, they do not address the question of ....

Trnen, P., Kolehmainen, M., Wong, G., Castren, E. Analysis of gene expression data using self-organizing maps. FEBS Lett.:451: 142-146: 1999.

.... applied to mine microarray data, are visual discovery and interpretation procedures [10, 13] singular value decomposition and projection on principal component planes [2, 20, 28] supervised machine learning techniques [6, 15] Fourier analysis [31] relevance networks [7 8] self organizing maps [17, 34 35], procedures based on network inference [11] and Shannon entropy calculations [14] Among all, the most widely used technique is hierarchical agglomerative clustering. As reported in many publications, clustering techniques have been applied to identify groups of genes sharing similar expression ....

Toronen, P., Kolehmainen, M., Wong, G., and Castren, E. (1999) Analysis of gene expression data using selforganizing maps. FEBS Letters, 451(2), 142-146.

....[27] This data is replete with undiscovered biological knowledge which holds the promise of revolutionising biotechnology and medicine. KDD techniques are well suited to extracting this knowledge. Currently most KDD analysis of bioinformatic data has been based on using unsupervised methods e.g. [9, 17, 32], but some has been based on supervised methods [4, 7, 14] New KDD methods are constantly required to meet the new challenges presented by new forms of bioinformatic data. Perhaps the least analysed form of genomics data is that from phenotype experiments [25, 22, 18] In these experiments speci ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Lett., 451(2):142-6, May 1999.

.... of reevaluating the results in light of the complete clustering of the data, can cause some clusters of patterns to be based on local decisions rather than on the global picture (Tamayo et al. 1999) Other different clustering methods have recently been proposed (Heyer et al. 1999; Ben Dor et al. 1999), but their performance remains to be evaluated by the user community. These arguments lead to the use of neural networks as an alternative to hierarchical cluster methods (Tamayo et al. 1999; T or onen et al. 1999) Unsupervised neural networks, and in particular self Organising Maps (SOM) ....

T or onen,P., Kolehmainen,M., Wong,G. and Castr en,E. (1999) Analysis of gene expression data using self-organizing maps. FEBS Lett., 451, 142--146.

....Currently the challenges in genetics are shifting from analyzing the genetic sequences to analyzing the function of the genes. The Self Organizing Map (SOM) has potential as a tool in the required large scale analyses. SOMs have already been applied to clustering of yeast gene expression data [8,9]. In this paper SOM based methods for exploratory data analysis will be described and applied to yeast gene expression analysis. We have developed methods for uncovering and visualizing cluster structures in an easily understandable manner, and for interpreting them in terms of the original data ....

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS Letters, 451:142-146, 1999.

No context found.

T or onen, P., Kolehmainen, M., Wong, G., Castren, E., 1999. Analysis of gene expression data using self-organizing maps. Federation of European Biochemical Societies Letters 451, 142-14.

....of the Clusters The clusters found by the U matrix and our enhanced method (Fig. 1) have traditionally been analyzed with three methods: 1) By plotting class distributions as we have done above, 2) By plotting the model vectors, in this case the expression pro les represented by the map units [12, 13], and (3) by plotting the distribution of the original data variables, in the present paper the expression levels at a certain treatment and a time point, on the map. The problem with the approaches (2) and (3) for large maps and high dimensional data, is that they present a huge amount of data. ....

P. T or onen, M. Kolehmainen, G. Wong, and E. Castr en, \Analysis of gene expression data using selforganizing maps," FEBS Letters, vol. 451, pp. 142-6, 1999.

No context found.

Trnen, P., Kolehmainen, M., Wong, G., and Castrn, E. (1999): Analysis of gene expression data using selforganising maps. FEBS Letters, 451:142-146.

No context found.

Toronen, P., Kolehmainen, M., Wong, G., and Castren, E., Analysis of gene expression data using self-organizing maps. FEBS Lett, 1999. 451(2): p. 142-6.

No context found.

Trnen, P., Kolehmainen, M., Wong, G., and Castrn, E. (1999). Analysis of gene expression data using self-organizing maps. FEBS Letters, 451:142--146.

No context found.

P. Toronen, M. Kolehmainen, G. Wong, and E. Castren. Analysis of gene expression data using self-organizing maps. FEBS, 451:142--146, 1999. 3

No context found.

P. Toronen, M. Kolehmainen, G. Wong and E. Castren, Analysis of gene expression data using selforganizing maps. FEBS Letters, 451:142-146, 1999.

No context found.

Toronen, P., M. Kolehmainen, G. Wong, and E. Castren. 1999. Analysis of gene expression data using self-organizing maps. FEBS Lett. 451:142-146.

No context found.

Toronen P, Kolehmainen M, Wong G, Castren E: Analysis of gene expression data using self-organizing maps. FEBS Letters 1999, 451:142-146

No context found.

Toronen, P., Kolehmainen, M., Wong, G. & Castren, E. (1999). Analysis of gene expression data using self-organizing maps. FEBS Letters, 451, 142-146.

No context found.

Toronen P, Kolehmainen M, Wong G, and Gastren E. Analysis of gene expression data using self-organizing maps. FEBS Letters 451: 142-146, 1999.

No context found.

Toronen, P., et al., Analysis of gene expression data using self-organizing maps. FEBS Lett, 1999. 451(2): p. 142-6.

No context found.

Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps. FEBS Lett 1999;451(2):142-6.

No context found.

Toronen P, Kolehmainen M, Wong G, Castren E. Analysis of gene expression data using self-organizing maps [In Process Citation]. FEBS Lett 1999;451(2):142-6.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC