Optimized Substructure Discovery for Semi-structured Data

Abe, Kenji; Kawasoe, Shinji; Asai, Tetsuya; Arimura, Hiroki; Arikawa, Setsuo

doi:10.1007/3-540-45681-3_1

Cited by 68 publications

(57 citation statements)

References 22 publications

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“…The binding sites of all the proteins are known, but the binding site extraction is conducted under the assumption that their binding sites are unknown. We ranked the optimal graphs extracted from each pocket based on the value of the evaluation function defined in (6). Fig.…”

Section: Experiments and Discussionmentioning

confidence: 99%

“…These intuitive statements are formalized as follows. Certain subgraph I of a pocket in target protein T divides a set of referential proteins R into two subsets, We introduce an evaluation function, where a subgraph satisfying the above requirements has better value, based on a method proposed by Abe et al [6]:…”

Section: A Detecting Optimal Graphsmentioning

confidence: 99%

“…Since the evaluation function proposed by Abe et al [6] that compares and assesses the optimality of the extracted patterns assumes that the number of positive and negative instances are unchanged, it cannot directly compare an optimal graph obtained as a result of group integration with an optimal graph without group integration. Therefore, in this research, we introduce another evaluation function that focuses on the distance from the subgraph that scores the worst evaluation value.…”

Section: Introductionmentioning

confidence: 99%

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Binding Site Extraction by Detecting Optimal Graphs from Protein Molecular Surfaces

Mitsui¹,

Ohkawa²

2014

IJBBB

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Abstract-Proteins fulfill their functions by binding with molecular compounds called ligands. This research automatically extracts a binding site from the surface of a protein. A binding site candidate can be extracted as the local portion that satisfies the following two requirements. One is the structural similarity among proteins that bind the same kind of ligands. The other is the structural dissimilarity between a binding site in a protein and any local surfaces in the proteins that bind to any other ligands. By representing a protein molecular surface as a graph, the binding site extraction problem can be regarded as an optimal subgraph detection problem in which the best subgraph is extracted that satisfies the above requirements. However, if two ligands are different but have a partly similar structure, the binding sites of the proteins that bind these ligands often resemble each other. In such situations, an optimal graph may not present the binding site. Therefore, we introduce the concept of group integration, in which more than one group with similar ligands partners is regarded as a positive group. As a result of group integration, the number of proteins in a positive group and in a negative group is changed. Therefore, based on the distance from the virtual worst subgraph, an evaluation function is introduced to compare subgraphs with and without group integration. We clarified the effectiveness of binding site extraction with group integration through an experiment with 37 proteins.

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Section: Experiments and Discussionmentioning

confidence: 99%

Section: A Detecting Optimal Graphsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Binding Site Extraction by Detecting Optimal Graphs from Protein Molecular Surfaces

Mitsui¹,

Ohkawa²

2014

IJBBB

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“…This results in a high memory consumption [17,1]. To control memory consumption in our analysis we restrict ourselves to mining neighborhoods of a particular size l, i.e., neighborhoods that include no more that l function calls.…”

Section: Filtering Proceduresmentioning

confidence: 99%

“…In our experimental tests we use the frequent pattern mining algorithm FREQT, independently introduced by Asai et al [1] and Zaki [17]. It efficiently extracts frequent ordered labeled subtrees from a database of rooted ordered labeled trees.…”

Section: Time Complexitymentioning

confidence: 99%

Discriminative pattern mining in software fault detection

Fatta

Leue

Stegantova

2006

Proceedings of the 3rd International Workshop on Software Quality Assurance

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We present a method to enhance fault localization for software systems based on a frequent pattern mining algorithm. Our method is based on a large set of test cases for a given set of programs in which faults can be detected. The test executions are recorded as function call trees. Based on test oracles the tests can be classified into successful and failing tests. A frequent pattern mining algorithm is used to identify frequent subtrees in successful and failing test executions. This information is used to rank functions according to their likelihood of containing a fault. The ranking suggests an order in which to examine the functions during fault analysis. We validate our approach experimentally using a subset of Siemens benchmark programs.

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