Structure-based prediction for the site of metabolism (SOM) of a compound metabolized by human cytochrome P450s (CYPs) is highly beneficial in drug discovery and development. However, the flexibility of the CYPs' active site remains a huge challenge for accurate SOM prediction. Compared with other CYPs, the active site of CYP2A6 is relatively small and rigid. To address the impact of the flexib...

With increases in the amounts of available DNA sequence data, it has become increasingly important to develop tools for comprehensive systematic analysis and comparison of species-specific characteristics of protein-coding sequences for a wide variety of genomes. In the present study, we used a novel neural-network algorithm, a self-organizing map (SOM), to efficiently and comprehensively analy...

The Self-Organizing Map, SOM, is a widely used tool in exploratory data analysis. Visual inspection of the SOM can be used to list potential dependencies between variables, that are then validated with more principled statistical methods. In this paper we discuss the use of the SOM in searching for dependencies in the data. We point out that simple use of the SOM may lead to excessive number of...

We present a new neural classification model called Concurrent Self-Organizing Maps (CSOM), representing a winner-takes-all collection of small SOM networks. Each SOM of the system is trained individually to provide best results for one class only. We have considered two significant applications: face recognition and multispectral satellite image classification. For first application, we have u...

The Self-Organizing Map (SOM) is a powerful tool in the exploratory phase of data mining. It is capable of projecting high-dimensional data onto a regular, usually 2dimensional grid of neurons with good neighborhood preservation between two spaces. However, due to the dimensional conflict, the neighborhood preservation cannot always lead to perfect topology preservation. In this paper, we estab...

The effect of decomposition and diagenesis of sediment organic matter (SOM) on the adsorption of emerging pollutants by the sediment has been seldom addressed. In the present experimental study, artificial sediment was incubated to simulate the natural organic diagenesis process and hence investigate the influence of organic diagenesis on the adsorption of tetracyclines (TCs) by marine sediment...

Protein fold classification is an important problem in bioinformatics and a challenging task for machine-learning algorithms. In this paper we present a solution which classifies protein folds using Kohonen’s Self-Organizing Map (SOM) and a comparison between few approaches for protein fold classification. We use SOM, Fisher Linear Discriminant Analysis (FLD), K-Nearest Neighbour (KNN), Support...

This paper describes the application of the Self-Organizing Map (SOM) in visual exploration of physical geography data. The main justifications for the application of SOM in this issue is that its stresses local factors and topological order. Public domain thematic maps from Portuguese Environment Institute are used. An adequate geospatial unfolding of SOM is presumed to assist a better represe...

It is expected that the agricultural intensification occurred in recent decades in the Argentine Rolling Pampa significantly alters the SOM reserves. Therefore, it is necessary to identify soil organic carbon (C) and nitrogen (N) fractions to understand the functionality and stabilization of these reserves. Our objectives were to study the NT effect in two crop rotations, corn-double cropped wh...

—This paper presents a constrained self-organizing map (SOM) model for the visualization and reconstruction of the human brain lateral ventricle. The SOM model is a widely used method to approximate large and complex high dimensional data and reduce the data dimension for advanced applications. In our applications, the SOM model is used to deform a spherical network field to a 3D crooked brain ...