Visualization and Grouping of Graph Patterns in Molecular Databases

Graaf, Edgar H. de; Kosters, Walter A.; Kok, Joost N.; Kazius, Jeroen

doi:10.1007/978-1-84800-094-0_20

Cited by 3 publications

(4 citation statements)

References 16 publications

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“…In the field of cheminformatics, many applications benefit from these highly efficient methods. Frequent substructure mining techniques are, e.g., applied for molecule data set visualization, clustering, , or efficient database searching . However, a common problem of frequent substructure mining is its inherent characteristic that exact subgraph matching prevents the identification of similar properties resulting from chemically related elements.…”

Section: Introductionmentioning

confidence: 99%

Discriminative Chemical Patterns: Automatic and Interactive Design

Bietz

Schomburg

Hilbig

et al. 2015

J. Chem. Inf. Model.

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The classification of molecules with respect to their inhibiting, activating, or toxicological potential constitutes a central aspect in the field of cheminformatics. Often, a discriminative feature is needed to distinguish two different molecule sets. Besides physicochemical properties, substructures and chemical patterns belong to the descriptors most frequently applied for this purpose. As a commonly used example of this descriptor class, SMARTS strings represent a powerful concept for the representation and processing of abstract chemical patterns. While their usage facilitates a convenient way to apply previously derived classification rules on new molecule sets, the manual generation of useful SMARTS patterns remains a complex and time-consuming process. Here, we introduce SMARTSminer, a new algorithm for the automatic derivation of discriminative SMARTS patterns from preclassified molecule sets. Based on a specially adapted subgraph mining algorithm, SMARTSminer identifies structural features that are frequent in only one of the given molecule classes. In comparison to elemental substructures, it also supports the consideration of general and specific SMARTS features. Furthermore, SMARTSminer is integrated into an interactive pattern editor named SMARTSeditor. This allows for an intuitive visualization on the basis of the SMARTSviewer concept as well as interactive adaption and further improvement of the generated patterns. Additionally, a new molecular matching feature provides an immediate feedback on a pattern's matching behavior across the molecule sets. We demonstrate the utility of the SMARTSminer functionality and its integration into the SMARTSeditor software in several different classification scenarios.

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Section: Introductionmentioning

confidence: 99%

Discriminative Chemical Patterns: Automatic and Interactive Design

Bietz

Schomburg

Hilbig

et al. 2015

J. Chem. Inf. Model.

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“…Some notable system in this field includes Prov-O-Viz [78] and Orbiter [101]. Some systems are not limited to the usage in the data provenance field, there include some tools for chemical and biological structure visualization [67] which also suitable to visualize and analyse provenance data.…”

Section: Provenance Systems and Toolsmentioning

confidence: 99%

Analysing system behaviour by automatic benchmarking of system-level provenance

Chan

2019

Proceedings of the 20th International Middleware Conference Doctoral Symposium

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When referring to this work, full bibliographic details including the author, title, awarding institution and date of the thesis must be given. I am extremely grateful to many people for their help in completing my PhD. In particular, I would like to thank the following people: Thank you to my supervisors James Cheney, David Aspinall and Pramod Bhatotia for their advice, criticism, help, patience, and giving me the chance to work as a PhD under their supervision. Thank you to Ashish Gehani, Hassaan Irshad and researchers in SRI International for the helpful discussions in early PhD. Another thank you to Ashish Gehani and Hassaan Irshad, together with Thomas Pasquier, Margo Seltzer, Lucian Carata, Ripduman Sohan and researchers of SPADE, OPUS and CamFlow for helping me to test the ProvMark tools on their provenance systems and support me in the publication for ProvMark. Thank you to Myrto Arapinis and Ajitha Rajan for providing valuable opinions in the year review meeting. Thank you to Ajitha Rajan and Adam Bates, examiners of my viva, for providing valuable comments and suggestions on my PhD project and thesis.

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“…One of the techniques in this framework is the analysis of graphs in which frequent subgraphs occur, via competitive neural networks as presented in [1]. Another important functionality is the browsing of lattice information from parent to child and from one group of fragments to another as presented here.…”

Section: Introductionmentioning

confidence: 99%

“…The algorithm for grouping was also used in [1], both papers discuss a component of the same framework. However in this work groups are used differently, for fragment suggestion during browsing.…”

Section: Introductionmentioning

confidence: 99%

Clustering Improves the Exploration of Graph Mining Results

Graaf

Kok

Kosters

2007

IFIP the International Federation for Information Processing

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Abstract. Mining frequent subgraphs is an area of research where we have a given set of graphs, and where we search for (connected) subgraphs contained in many of these graphs. Each graph can be seen as a transaction, or as a molecule -as the techniques applied in this paper are used in (bio)chemical analysis. In this work we will discuss an application that enables the user to further explore the results from a frequent subgraph mining algorithm. Such an algorithm gives the frequent subgraphs, also referred to as fragments, in the graphs in the dataset. Next to frequent subgraphs the algorithm also provides a lattice that models sub-and supergraph relations among the fragments, which can be explored with our application. The lattice can also be used to group fragments by means of clustering algorithms, and the user can easily browse from group to group. The application can also display only a selection of groups that occur in almost the same set of molecules, or on the contrary in different molecules. This allows one to see which patterns cover different or similar parts of the dataset.

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Visualization and Grouping of Graph Patterns in Molecular Databases

Cited by 3 publications

References 16 publications

Discriminative Chemical Patterns: Automatic and Interactive Design

Discriminative Chemical Patterns: Automatic and Interactive Design

Analysing system behaviour by automatic benchmarking of system-level provenance

Clustering Improves the Exploration of Graph Mining Results

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