Emergence in Self Organizing Feature Maps

This paper sheds some light on the differences between SOM and emergent SOM (ESOM). The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The epistemological postulates regarding this issue are hard, if not impossible, to prove. An alternative postulate relying on semiotic concepts, called "semiotic irreducibility" is proposed here. This concept is applied to U-Matrix on SOM with many neurons. This leads to the definition of ESOM as SOM producing a nontrivial U-Matrix on which the terms "watershed" and "catchment basin" are meaningful and which are cluster conform. The usefulness of the approach is demonstrated with an ESOM clustering algorithm which exploits the emergent properties of such SOM. Results on synthetic data also in blind studies are convincing. The application of ESOM clustering for a real world problem let to an excellent solution

Emergence in Self Organizing Feature Maps

This paper sheds some light on the differences between SOM and emergent SOM (ESOM). The discussion in philosophy and epistemology about Emergence is summarized in the form of postulates. The properties of SOM are compared to these postulates. SOM fulfill most of the postulates. The epistemological postulates regarding this issue are hard, if not impossible, to prove. An alternative postulate relying on semiotic concepts, called "semiotic irreducibility" is proposed here. This concept is applied to U-Matrix on SOM with many neurons. This leads to the definition of ESOM as SOM producing a nontrivial U-Matrix on which the terms "watershed" and "catchment basin" are meaningful and which are cluster conform. The usefulness of the approach is demonstrated with an ESOM clustering algorithm which exploits the emergent properties of such SOM. Results on synthetic data also in blind studies are convincing. The application of ESOM clustering for a real world problem let to an excellent solution

SOME EMPIRICAL RELATIONS BETWEEN TRAVEL SPEED, TRAFFIC VOLUME AND TRAFFIC COMPOSITION IN URBAN ARTERIALS

The effects of traffic mix (the percentage of cars, trucks, buses and so on) are of particular interest in the speed-volume relationship in urban signalized arterials under various geometric and control characteristics. The paper presents some empirical observations on the relation between travel speed, traffic volume and traffic composition in urban signalized arterials. A methodology based on emerging self-organizing structures of neural networks to identify regions in the speed-volume relationship with respect to traffic composition and Bayesian networks to evaluate the effect of different types of motorized vehicles on prevailing traffic conditions is proposed. Results based on data from a large urban network indicate that the variability in traffic conditions can be described by eight regions in speed-volume relationship with respect to traffic composition. Further evaluation of the effect of motorized vehicles in each region separately indicates that the effect of traffic composition decreases with the onset of congestion. Moreover, taxis and motorcycles are the primary affecting parameter of the form of the speed-volume relationship in urban arterials. Document type: Articl

Projection-Based Clustering through Self-Organization and Swarm Intelligence

It covers aspects of unsupervised machine learning used for knowledge discovery in data science and introduces a data-driven approach to cluster analysis, the Databionic swarm (DBS). DBS consists of the 3D landscape visualization and clustering of data. The 3D landscape enables 3D printing of high-dimensional data structures. The clustering and number of clusters or an absence of cluster structure are verified by the 3D landscape at a glance. DBS is the first swarm-based technique that shows emergent properties while exploiting concepts of swarm intelligence, self-organization and the Nash equilibrium concept from game theory. It results in the elimination of a global objective function and the setting of parameters. By downloading the R package DBS can be applied to data drawn from diverse research fields and used even by non-professionals in the field of data mining

Projection-Based Clustering through Self-Organization and Swarm Intelligence: Combining Cluster Analysis with the Visualization of High-Dimensional Data

Cluster Analysis; Dimensionality Reduction; Swarm Intelligence; Visualization; Unsupervised Machine Learning; Data Science; Knowledge Discovery; 3D Printing; Self-Organization; Emergence; Game Theory; Advanced Analytics; High-Dimensional Data; Multivariate Data; Analysis of Structured Dat

Secbase:A Novel Tool to Correlate Secondary Structure Elements with Ligand Binding

The main focus of this work is the development of new methods for computer aided drug design. This development and the final tools are described together with studies to show possible applications and to prove the usefulness of these tools. The first part of this work describes the integration of secondary structure element information together with geometric descriptions into a protein-ligand database (Relibase), which leads to the new modul Secbase. The python-based interface to Relibase (Reliscript) was used to add the information. Furthermore, the C++ core code of Relibase was extended to get access to this data and to add Secbase constraints to the substructure search. This leads to the opportunity to specifiy Secbase constraints within the substructure search accessible through the Relibase webinterface. The motivation for the development of Secbase is guided through two main ideas: Firstly, Secbase should provide means to analyse protein-ligand interactions with respect to secondary structure elements and, secondly, should allow analysis and discovery of functional similarity within related folding patterns. This is based on the knowledge that the function of a protein is often based on the structure and the spatial structure is more conserved in evolution than amino acid sequence. In general, Secbase, in combination with Relibase, can be used for knowledge discovery about the influence of secondary structural elements on protein-ligand interactions and should be valuable for structure based drug design and molecular modelling. Two major analyses were carried out using Reliscript and the Relibase Webinterface. The first analysis revealed some notable trends in hydrogen bonding geometry across the different secondary structural elements. The mean hydrogen bond length of accumulated hydrogen bonds in α-helices and parallel β-sheets decrease with increasing number of helix-turns and number of β-strands, respectively. The cooperative effect, which leads to a decrease of the mean hydrogen bond length, can be explained by a similar directionality of the peptide bond dipole vectors and the backbone hydrogen bonds in α-helices and parallel β-sheets. The second analysis describes a survey of water molecules next to the N-terminus of an α-helix and shows their involvement in ligand binding. Furthermore, the kinked backbone shows interactions between two neighboured backbone amide groups and carboxylate or phosphate groups, respectively. In agreement with theoretical calculations described in the literature, this analysis suggest that the first/last turn of an α-helix is the main source for charge stabilising effects, mainly by providing hydrogen bonds. This is in contrast to the widely used explanantion that the overall dipole of the helix has an influence. The second part of this work deals with turns as an irregular secondary structure element with a hydrogen bond or a specific Cα-Cα distance between the first and the last residue. Because of the irregularity, the classification into subfamilies changed over the last decades with growing data from protein structures and is not completely adapted to the actual data basis, yet. Additionally, there was a lack of an overall classification for all turn families. Therefore, a uniform classification for all normal (COi - NHi+n hydrogen bond), open (a Cαi-Cαi+n distance up to 10 Å) and reverse (NHi - COi+n hydrogen bond) turn families is presented based on current structural data. The emergent self-organizing maps (ESOM) were used to cluster all turn-conformations of a non-redundant protein chain dataset. In combination with β-sheet and helix classification on average 96% of the given protein chain is now successfully classified. The classification can be used for the identification of similar protein domains or structural motifs within different turn families and accordingly for the understanding of protein-ligand and protein-protein interactions. The created turn classification was used to classify the turn conformations within all protein structures. This information was also added to Secbase. Protein sequence-based turn prediction with high accuracy has already confirmed this new categorization based on machine learning methods as consistent and well-defined. Hopefully, this classification will also be supportive for protein fold and structure prediction