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182
Substructure similarity search in graph databases
- In SIGMOD
, 2005
"... Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, chem-informatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact mat ..."
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Cited by 90 (6 self)
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Advanced database systems face a great challenge raised by the emergence of massive, complex structural data in bioinformatics, chem-informatics, and many other applications. The most fundamental support needed in these applications is the efficient search of complex structured data. Since exact matching is often too restrictive, similarity search of complex structures becomes a vital operation that must be supported efficiently. In this paper, we investigate the issues of substructure similarity search using indexed features in graph databases. By transforming the edge relaxation ratio of a query graph into the maximum allowed missing features, our structural filtering algorithm, called Grafil, can filter many graphs without performing pairwise similarity computations. It is further shown that using either too few or too many features can result in poor filtering performance. Thus the challenge is to design an effective feature set selection strategy for filtering. By examining the effect of different feature selection mechanisms, we develop a multi-filter composition strategy, where each filter uses a distinct and complementary subset of the features. We identify the criteria to form effective feature sets for filtering, and demonstrate that combining features with similar size and selectivity can improve the filtering and search performance significantly. Moreover, the concept presented in Grafil can be applied to searching approximate non-consecutive sequences, trees, and other complicated structures as well. 1.
RASCAL: Calculation of Graph Similarity using Maximum Common Edge Subgraphs
, 2002
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Do structurally similar molecules have similar biological activity?
- J. Med. Chem.
, 2002
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Graph database indexing using structured graph decomposition
- In ICDE
, 2007
"... We introduce a novel method of indexing graph databases in order to facilitate subgraph isomorphism and similarity queries. The index is comprised of two major data structures. The primary structure is a directed acyclic graph which contains a node for each of the unique, induced subgraphs of the da ..."
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Cited by 57 (5 self)
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We introduce a novel method of indexing graph databases in order to facilitate subgraph isomorphism and similarity queries. The index is comprised of two major data structures. The primary structure is a directed acyclic graph which contains a node for each of the unique, induced subgraphs of the database graphs. The secondary structure is a hash table which crossindexes each subgraph for fast isomorphic lookup. In order to create a hash key independent of isomorphism, we utilize a code-based canonical representation of adjacency matrices, which we have further refined to improve computation speed. We validate the concept by demonstrating its effectiveness in answering queries for two practical datasets. Our experiments show that for subgraph isomorphism queries, our method outperforms existing methods by more than an order of magnitude. 1.
B.: STITCH: interaction networks of chemicals and proteins. Nucleic Acids Res. 36(Jan
, 2008
"... The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search ..."
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Cited by 56 (9 self)
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The knowledge about interactions between proteins and small molecules is essential for the understanding of molecular and cellular functions. However, information on such interactions is widely dispersed across numerous databases and the literature. To facilitate access to this data, STITCH (‘search tool for interactions of chemicals’) integrates information about interactions from metabolic pathways, crystal structures, binding experiments and drug–target relationships. Inferred information from phenotypic effects, text mining and chemical structure similarity is used to predict relations between chemicals. STITCH further allows exploring the network of chemical relations, also in the context of associated binding proteins. Each proposed interaction can be traced back to the original data sources. Our database contains interaction information for over 68 000 different chemicals, including 2200 drugs, and connects them to 1.5 million genes across 373 genomes and their interactions contained in the STRING data-base. STITCH is available at
Comparison of descriptor spaces for chemical compound retrieval and classification
- International Conference in Datamining. (ICDM
, 2006
"... Abstract. In recent years the development of computational techniques that build models to correctly assign chemical compounds to various classes or to retrieve potential drug-like compounds has been an active area of research. Many of the bestperforming techniques for these tasks utilize a descript ..."
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Cited by 42 (5 self)
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Abstract. In recent years the development of computational techniques that build models to correctly assign chemical compounds to various classes or to retrieve potential drug-like compounds has been an active area of research. Many of the bestperforming techniques for these tasks utilize a descriptor-based representation of the compound that captures various aspects of the underlying molecular graph’s topology. In this paper we compare five different set of descriptors that are currently used for chemical compound classification. We also introduce four different descriptors derived from all connected fragments present in the molecular graphs primarily for the purpose of comparing them to the currently used descriptor spaces and analyzing what properties of descriptor spaces are helpful in providing effective representation for molecular graphs. In addition, we introduce an extension to existing vector-based kernel functions to take into account the length of the fragments present in the descriptors. We experimentally evaluate the performance of the previously introduced and the new descriptors in the context of SVM-based classification and ranked-retrieval on 28 classification and retrieval problems derived from 18 datasets. Our experiments show that for both of these tasks, two of the four descriptors introduced in this paper along with the extended connectivity fingerprint based descriptors consistently and statistically outperform previously developed schemes based on the widely used fingerprint- and Maccs keys-based descriptors, as well as recently introduced descriptors obtained by mining and analyzing the structure of the molecular graphs.
A novel spectral coding in a large graph database
- In Proceedings of the International Conference on Extending Database Technology
, 2008
"... Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications, such as drug discovery and structural pattern recognition. Because sub-graph isomorphism is a NP-complete problem [4], we have to employ a filter-and-verification framework ..."
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Cited by 34 (2 self)
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Retrieving related graphs containing a query graph from a large graph database is a key issue in many graph-based applications, such as drug discovery and structural pattern recognition. Because sub-graph isomorphism is a NP-complete problem [4], we have to employ a filter-and-verification framework to speed up the search efficiency, that is, using an effective and efficient pruning strategy to filter out the false positives (graphs that are not possible in the results) as many as possible first, then validating the remaining candidates by subgraph isomorphism checking. In this paper, we propose a novel filtering method, a spectral encoding method, i.e. GCoding. Specifically, we assign a signature to each vertex based on its local structures. Then, we generate a spectral graph code by combining all vertex signatures in a graph. Based on spectral graph codes, we derive a necessary condition for sub-graph isomorphism. Then we propose two pruning rules for sub-graph search problem, and prove that they satisfy the no-false-negative requirement (no dismissal in answers). Since graph codes are in numerical space, we take this advantage and conduct efficient filtering over graph codes. Extensive experiments show that GCoding outperforms existing counterpart methods. 1.
The ChEMBL bioactivity database: an update
- Nucleic Acids Res
, 2014
"... ChEMBL is an open large-scale bioactivity database ..."
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Cited by 33 (6 self)
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ChEMBL is an open large-scale bioactivity database
Virtual screening of molecular databases using a support vector machine
- J. Chem. Inf. Model. 2005
"... The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to t ..."
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Cited by 32 (2 self)
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The Support Vector Machine (SVM) is an algorithm that derives a model used for the classification of data into two categories and which has good generalization properties. This study applies the SVM algorithm to the problem of virtual screening for molecules with a desired activity. In contrast to typical applications of the SVM, we emphasize not classification but enrichment of actives by using a modified version of the standard SVM function to rank molecules. The method employs a simple and novel criterion for picking molecular descriptors and uses cross-validation to select SVM parameters. The resulting method is more effective at enriching for active compounds with novel chemistries than binary fingerprint-based methods such as binary kernel discrimination.
Comparing Stars: On Approximating Graph Edit Distance
, 2009
"... Graph data have become ubiquitous and manipulating them based on similarity is essential for many applications. Graph edit distance is one of the most widely accepted measures to determine similarities between graphs and has extensive applications in the fields of pattern recognition, computer visio ..."
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Cited by 29 (0 self)
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Graph data have become ubiquitous and manipulating them based on similarity is essential for many applications. Graph edit distance is one of the most widely accepted measures to determine similarities between graphs and has extensive applications in the fields of pattern recognition, computer vision etc. Unfortunately, the problem of graph edit distance computation is NP-Hard in general. Accordingly, in this paper we introduce three novel methods to compute the upper and lower bounds for the edit distance between two graphs in polynomial time. Applying these methods, two algorithms AppFull and AppSub are introduced to perform different kinds of graph search on graph databases. Comprehensive experimental studies are conducted on both real and synthetic datasets to examine various aspects of the methods for bounding graph edit distance. Result shows that these methods achieve good scalability in terms of both the number of graphs and the size of graphs. The effectiveness of these algorithms also confirms the usefulness of using our bounds in filtering and searching of graphs.
