Clustering and similarity of chemical structures represented by binary substructure descriptors

Scsibrany, H.; Karlovits, M.; Demuth, W.; Muller, F.J.; Вармуза, Курт

doi:10.1016/s0169-7439(03)00054-6

Cited by 19 publications

(10 citation statements)

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“…Classification of the presence or absence of substructures based on mass spectral data has found great interest and has been suggested as a tool in systematic structure elucidation of organic molecules (Chapman, 1993;Klawun & Wilkins, 1996;Varmuza & Werther, 1996;Varmuza, 2000). Recently, exploratory data analysis methods have proven to be useful in the evaluation of database search results or spectra similarity hitlists, as well as for the recognition of spectra-structure relationships Scsibrany et al, 2003).…”

Section: A Overviewmentioning

confidence: 99%

Exploration of anthropological specimens by GC‐MS and chemometrics

Вармуза

Makristathis

Schwarzmeier

et al. 2004

Mass Spectrometry Reviews

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Anthropological specimens combine a variety of unfavorable characteristics, rendering their evaluation an analytical challenge. Their remarkable status is primarily based on two characteristics: (i) these very rare samples of human origin are testimonies of human history and are, therefore, available only in minute amounts for analytical purposes, and (ii) the analysis of these samples is extremely limited by the decomposition of molecules, which are easily detected in living organisms, such as nucleic acids and proteins, but are subject to rapid post-mortem decay. In this article, we review the methods and results of archaeometry, emphasizing the role of MS combined with chemometrics. Focusing on experimental results for fatty acid profiles, specimens from mummies from different civilizations were compared. Considering in particular the Tyrolean Iceman, the application of chemometric methods to GC-MS data recovers essential information about the preservation and the storage conditions of mummies.

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Section: A Overviewmentioning

confidence: 99%

Exploration of anthropological specimens by GC‐MS and chemometrics

Вармуза

Makristathis

Schwarzmeier

et al. 2004

Mass Spectrometry Reviews

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“…The Ames database was created from six different public sources [ 40 , 47 ] and each chemical structure possesses a class label (0 and 1) that results from the Ames test indicating the genetoxicity of a substance. By starting from the original database Ames mutagenicity [ 40 , 47 ] containing 6512 chemical compounds, we created AG 3982 by filtering out isomorphic graphs based on the software SubMat [ 86 ]. Finally, this procedure resulted in 3982 structurally different skeletons, that is, all atoms and all bonds are considered as equal.…”

Section: Resultsmentioning

confidence: 99%

“…Further, topological descriptors have often been combined with other techniques from statistical data analysis, e.g., clustering methods [ 26 , 48 ] to infer correlations between the used indices. Besides using topological descriptors for characterizing chemical graphs [ 27 , 32 , 49 ], they have also been applied to quantify the structural similarity of chemicals representing networks [ 50 , 51 ]. Among the large number of existing topological indices, an important class of such measures relies on S HANNON 's entropy to characterize graphs by determining their structural information content [ 27 , 52 - 54 ].…”

Section: Introductionmentioning

confidence: 99%

Novel topological descriptors for analyzing biological networks

et al. 2010

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BackgroundTopological descriptors, other graph measures, and in a broader sense, graph-theoretical methods, have been proven as powerful tools to perform biological network analysis. However, the majority of the developed descriptors and graph-theoretical methods does not have the ability to take vertex- and edge-labels into account, e.g., atom- and bond-types when considering molecular graphs. Indeed, this feature is important to characterize biological networks more meaningfully instead of only considering pure topological information.ResultsIn this paper, we put the emphasis on analyzing a special type of biological networks, namely bio-chemical structures. First, we derive entropic measures to calculate the information content of vertex- and edge-labeled graphs and investigate some useful properties thereof. Second, we apply the mentioned measures combined with other well-known descriptors to supervised machine learning methods for predicting Ames mutagenicity. Moreover, we investigate the influence of our topological descriptors - measures for only unlabeled vs. measures for labeled graphs - on the prediction performance of the underlying graph classification problem.ConclusionsOur study demonstrates that the application of entropic measures to molecules representing graphs is useful to characterize such structures meaningfully. For instance, we have found that if one extends the measures for determining the structural information content of unlabeled graphs to labeled graphs, the uniqueness of the resulting indices is higher. Because measures to structurally characterize labeled graphs are clearly underrepresented so far, the further development of such methods might be valuable and fruitful for solving problems within biological network analysis.

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“…Using this matrix classical clustering algorithms can be applied [70][71][72], or also, previously reducing this representational space by applying a PCA over the n-dimensional descriptors representation [73].…”

Section: Pattern Representation Of Chemicals Structuresmentioning

confidence: 99%