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Kraus V.,Division for Bioinformatics and Translational Research | Dehmer M.,Division for Bioinformatics and Translational Research | Dehmer M.,University of Federal Defense Munich | Emmert-Streib F.,Center for Cancer Research and Cell Biology
Information Sciences | Year: 2014

Proving interrelations between structural graph measures analytically has been intricate. Generally, relations between structural graph measures describe the interplay between measures which turned out to be useful for better understanding the properties of such quantities. The results which have been achieved so far are restricted to simple measures or special graph classes such as trees. In this paper, we introduce a probabilistic approach for establishing inequalities between quantitative network measures on random networks. Those inequalities between different graph measures lead to a deeper understanding of the mathematical apparatus and, in particular, to a discussion of quality aspects of structural graph measures, which is a major contribution of this paper. © 2014 Elsevier Inc. All rights reserved.


Dehmer M.,Division for Bioinformatics and Translational Research | Emmert-Streib F.,Queen's University of Belfast | Tripathi S.,Division for Bioinformatics and Translational Research
PLoS ONE | Year: 2013

Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively. © 2013 Dehmer et al.


Dander A.,Division for Bioinformatics and Translational Research | Dander A.,Innsbruck Medical University | Dander A.,Center for Personalized Medicine | Mueller L.A.J.,Division for Bioinformatics and Translational Research | And 7 more authors.
Source Code for Biology and Medicine | Year: 2013

Background: Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging.Results: This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at. © 2013 Dander et al.; licensee BioMed Central Ltd.


Dehmer M.,University of Federal Defense Munich | Dehmer M.,Division for Bioinformatics and Translational Research | Emmert-Streib F.,Queen's University of Belfast | Shi Y.,Nankai University
PLoS ONE | Year: 2014

In this paper, we derive interrelations of graph distance measures by means of inequalities. For this investigation we are using graph distance measures based on topological indices that have not been studied in this context. Specifically, we are using the well-known Wiener index, Randić index, eigenvalue-based quantities and graph entropies. In addition to this analysis, we present results from numerical studies exploring various properties of the measures and aspects of their quality. Our results could find application in chemoinformatics and computational biology where the structural investigation of chemical components and gene networks is currently of great interest. © 2014 Dehmer et al.


PubMed | Division for Bioinformatics and Translational Research and Queen's University of Belfast
Type: Evaluation Studies | Journal: PloS one | Year: 2014

Molecular descriptors have been explored extensively. From these studies, it is known that a large number of descriptors are strongly correlated and capture similar characteristics of molecules. In this paper, we evaluate 919 Dragon-descriptors of 6 different categories by means of clustering. Also, we analyze these different categories of descriptors also find a subset of descriptors which are least correlated among each other and, hence, characterize molecular graphs distinctively.


PubMed | Division for Bioinformatics and Translational Research
Type: Journal Article | Journal: Source code for biology and medicine | Year: 2014

Molecular descriptors have been extensively used in the field of structure-oriented drug design and structural chemistry. They have been applied in QSPR and QSAR models to predict ADME-Tox properties, which specify essential features for drugs. Molecular descriptors capture chemical and structural information, but investigating their interpretation and meaning remains very challenging.This paper introduces a large-scale database of molecular descriptors called COMMODE containing more than 25 million compounds originated from PubChem. About 2500 DRAGON-descriptors have been calculated for all compounds and integrated into this database, which is accessible through a web interface at http://commode.i-med.ac.at.

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