Kohonen self-organized maps

This format was developed in our group and is used fruitfully in SONNIA, software for producing Kohonen Self Organizing Maps (KSOM) and Coimter-Propaga-tion (CPG) neural networks for chemical application [6]. This file format is ASCII-based, contains the entire information about patterns and usually comes with the extension "dat . 

Initially the dataset contained 818 compounds, among which 31 were active (high TA, low USE), 157 inactive (low TA, high USE), and the rest intermediate. When the complete dataset was employed, none of the active compounds and 47 of the inactives were correctly classified by using Kohonen self-organizing maps (KSOM). 

The Kohonen Self-Organizing Maps can be used in a. similar manner. Suppose Xj., k = 1,. Nis the set of input (characteristic) vectors, Wy, 1 = 1,. l,j = 1,. J is that of the trained network, for each (i,j) cell of the map N is the number of objects in the training set, and 1 and j are the dimensionalities of the map. Now, we can compare each with the Wy of the particular cell to which the object was allocated. This procedure will enable us to detect the maximal (e max) minimal ( min) errors of fitting. Hence, if the error calculated in the way just mentioned above is beyond the range between e and the object probably does not belong to the training population. 

It can be shown that the unsupervised learning methodology based on Kohonen self-organizing maps algorithm can be effectively used for differentiation between various receptor-specific groups of GPCR ligands. The method is similar to that described in Section 12.2.6. 

Another type of ANNs widely employed is represented by the Kohonen self organizing maps (SOMs), used for unsupervised exploratory analysis, and by the counterpropagation (CP) neural networks, used for nonlinear regression and classification (Marini, 2009). Also, these tools require a considerable number of objects to build reliable models and a severe validation. 

A. M. Fonseca, J. L. Vizcaya, J. Aires-de-Sousa and A. M. Lobo, Geographical classification of crude oils by Kohonen self-organizing maps, Anal. Chim. Acta, 556(2), 2006, 374-382. 

Principal component analysis and Kohonen self-organizing maps allow multivariate data to be displayed as a graph for direct viewing, thereby extending the ability of human pattern recognition to uncover obscure relationships in complex data sets. This enables the scientist or engineer to play an even more interactive role in the data analysis. Clearly, these two techniques can be very useful when an investigator believes that distinct class differences exist in a collection of samples but is not sure about the nature of the classes. 

Figure 4.5 Kohonen self-organizing map diagram. (Not all connections are shown).

Recent applications of Kohonen self-organizing maps include construction of phylogenetic trees from sequence information (Wang et al., 1998) predicting cleavage sites in protein (Cai et al., 1998a) prediction of beta-turns (Cai et al., 1998b) ... 

Kohonen self-organizing map An unsupervised learning method of clustering, based on the k-means algorithm, similar to the first stage of radial basis function networks. Self-organized maps are used for classification and clustering. 

T. Kohonen, Self-organizing Maps, Springer-Verlag, Berlin, 1995. 

MOLMAP (Molecular Map of Atom-level Properties) descriptors are uniform-length vectorial descriptors derived by mapping physico-chemical properties of all the bonds in a molecule into a 2D Kohonen —> self organizing map (SOM) [Zhang and Aires-de-Sousa, 2005 Gupta, Metthew ef al., 2006]. These descriptors encode local features of a chemical structure, being calculated on the basis of properties of single elements in a molecule, such as bonds. 

Key words Chemogenomics, Biological target, Neural modeling, Kohonen self-organized maps, GPCR, Compound library, Chemokine, Receptor... 

MI T. Kohonen, Self-Organizing Maps, 3rd ed., Springer Berlin (2001). 

To provide you with a solid basis for deciding whether or not a given ANN is appropriate for your intended use, we describe briefly in this section many of the types of ANNs that have appeared in the literature in the past few years. For each network we focus on strengths and weaknesses, some practical aspects of operation, and a literature review of the chemical applications. We do not delve into detailed mathematical descriptions of networks, since these can be found in any number of texts. In particular we call attention to Ref. 19, which offers step-by-step developments of equations and detailed numerical examples for backpropagation, biassociative memory, counterpropagation, Hopfield, and Kohonen self-organizing map networks. Adaptive resonance theory networks are reviewed in detail in Ref. 27. 

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