Maximum Common Subgraph Isomorphism Algorithms

Maximum common subgraph (MCS) isomorphism algorithms play an important role in chemoinformatics by providing an effective mechanism for the alignment of pairs of chemical structures. This article discusses the various types of MCS that can be identified when two graphs are compared and reviews some of the algorithms that are available for this purpose, focusing on those that are, or may be, applicable to the matching of chemical graphs

Comparison of maximum common subgraph isomorphism algorithms for the alignment of 2D chemical structures

The identification of the largest substructure in common when two (or more) molecules are overlaid is important for several applications in chemoinformatics, and can be implemented using a maximum common subgraph (MCS) algorithm. Many such algorithms have been reported, and it is important to know which are likely to be the useful in operation. A detailed comparison was hence conducted of the efficiency (in terms of CPU time) and the effectiveness (in terms of the size of the MCS identified) of eleven MCS algorithms, some of which were exact and some of which were approximate in character. The algorithms were used to identify both connected and disconnected MCSs on a range of pairs of molecules. The fastest exact algorithms for the connected and disconnected problems were found to be the fMCS and MaxCliqueSeq algorithms, respectively, while the ChemAxon_MCS algorithm was the fastest approximate algorithm for both types of problem

Towards population-based structural health monitoring, Part III: graphs, networks and communities

Population-based structural health monitoring opens up the possibility of using information from a population of structures to provide extra information for each individual structure. For example, population-based structural health monitoring could provide improved damage-detection within a homogeneous population of structures by defining a normal condition across a population of structures, which was robust to environmental variation. Furthermore, in cases where structures are sufficiently similar, damage location, assessment, and classification labels could be transferred, increasing the damage labels available for each structure. To determine whether two structures are sufficiently similar requires the comparison of some representation of the structure. In fields such as bioinformatics and computer science, attributed graphs are often used to determine structural similarity. This paper will describe methods for comparing the topology attributes of two such graphs. The algorithm described is suited to population-based structural health monitoring as it provides matches between two graphs which have physical significance. This paper will also describe the process of comparing hierarchical attributes to determine the level of knowledge transfer possible between two structures

The IntAct molecular interaction database in 2012

IntAct is an open-source, open data molecular interaction database populated by data either curated from the literature or from direct data depositions. Two levels of curation are now available within the database, with both IMEx-level annotation and less detailed MIMIx-compatible entries currently supported. As from September 2011, IntAct contains approximately 275 000 curated binary interaction evidences from over 5000 publications. The IntAct website has been improved to enhance the search process and in particular the graphical display of the results. New data download formats are also available, which will facilitate the inclusion of IntAct's data in the Semantic Web. IntAct is an active contributor to the IMEx consortium (http://www.imexconsortium.org). IntAct source code and data are freely available at http://www.ebi.ac.uk/intact

The Gene Ontology resource: enriching a GOld mine

The Gene Ontology Consortium (GOC) provides the most comprehensive resource currently available for computable knowledge regarding the functions of genes and gene products. Here, we report the advances of the consortium over the past two years. The new GO-CAM annotation framework was notably improved, and we formalized the model with a computational schema to check and validate the rapidly increasing repository of 2838 GO-CAMs. In addition, we describe the impacts of several collaborations to refine GO and report a 10% increase in the number of GO annotations, a 25% increase in annotated gene products, and over 9,400 new scientific articles annotated. As the project matures, we continue our efforts to review older annotations in light of newer findings, and, to maintain consistency with other ontologies. As a result, 20 000 annotations derived from experimental data were reviewed, corresponding to 2.5% of experimental GO annotations. The website (http://geneontology.org) was redesigned for quick access to documentation, downloads and tools. To maintain an accurate resource and support traceability and reproducibility, we have made available a historical archive covering the past 15 years of GO data with a consistent format and file structure for both the ontology and annotations

A genetic approach to the maximum common subgraph problem

Finding the maximum common subgraph of a pair of given graphs is a well-known task in theoretical computer science and with considerable practical applications, for example, in the fields of bioinformatics, medicine, chemistry, electronic design and computer vision. This problem is particularly complex and therefore fast heuristics are required to calculate approximate solutions. This article deals with a simple yet effective genetic algorithm that finds quickly a solution, subject to possible geometric constraint