Subgraph Isomorphism Research Articles

Derivatives of 3-Amino-2-methylpyridine as BAZ2B Bromodomain Ligands: In Silico Discovery and in Crystallo Validation.

The 3-amino-2-methylpyridine derivative 1 was identified as ligand of the BAZ2B bromodomain by automatic docking of nearly 500 compounds, selected on the basis of previous fragment hits. Hit expansion by two in silico approaches, pharmacophore search followed by docking, and substructure search resulted in five additional ligands. The predicted binding mode of the six 3-amino-2-methylpyridine derivatives was validated by protein crystallography. A small displacement of residues 1894-1899 of the ZA loop is observed for two of the six ligands. In all structures, the pyridine head is involved in a water-mediated hydrogen bond with the side chain of the conserved Tyr1901 while the 3-amino linker acts as hydrogen bond donor for the backbone carbonyl of Pro1888. Heterogeneous orientations are observed for the tail groups (i.e., the 3-amino substituents). The sulfonyl group in the tail of compounds 1 and 2 is involved in a hydrogen bond with the backbone amide of Asn1894.

Detecting subgraph isomorphism with MapReduce

In recent years, the MapReduce framework has become one of the most popular parallel computing platforms for processing big data. MapReduce is used by companies such as Facebook, IBM, and Google to...

Irregular architectural layout synthesis with graphical inputs

Abstract This paper presents a method for automatically constructing irregular floor plans. Given image patterns, the program produces a variety of layouts that satisfy users' geometric and topological requirements. The program extracts irregular regions from images via statistical region merging. It employs subgraph matching and simulated annealing to construct topologically feasible layouts. Methods for measuring similarities between the desired templates and irregular rooms are also reported. Applications using raster images and vector graphics are tested. Both applications generate feasible floor plans whose appearances resemble the input patterns. The results indicate that layout automation is valid without an orthogonal framework in the plane. Irregular layout synthesis allows architects to instantly implement visual preferences in early design stages.

Chemical Structure Similarity Search for Ligand-based Virtual Screening: Methods and Computational Resources.

For many years the assumption that "Chemical compounds with similar structures may have similar activities" has been a foundation for lead identification. The similarity can be computed based upon topological, steric, electronic, and/or physical properties. The chemical structure similarity search differs from the chemical substructure search in that the former requires assessment of the properties of each compound and thus no filter can be applied for skipping structures before they are assessed to accelerate the computation. The latter can be accelerated by pre-screening compounds and omitting those that miss one (or more) specified fragments from the query. Moreover, three-dimensional similarity search requires superimposing many conformation pairs for each compound in the library. This makes 3-D similarity search algorithms time-consuming, and in general requires high performance computing (HPC) resources. This review will summarize recent progress in the techniques for HPC-supported two and three-dimensional chemical structure similarity search algorithms, and their applications in ligand-based virtual screening.

Computational Analysis and Predictive Cheminformatics Modeling of Small Molecule Inhibitors of Epigenetic Modifiers.

BackgroundThe dynamic and differential regulation and expression of genes is majorly governed by the complex interactions of a subset of biomolecules in the cell operating at multiple levels starting from genome organisation to protein post-translational regulation. The regulatory layer contributed by the epigenetic layer has been one of the favourite areas of interest recently. This layer of regulation as we know today largely comprises of DNA modifications, histone modifications and noncoding RNA regulation and the interplay between each of these major components. Epigenetic regulation has been recently shown to be central to development of a number of disease processes. The availability of datasets of high-throughput screens for molecules for biological properties offer a new opportunity to develop computational methodologies which would enable in-silico screening of large molecular libraries.MethodsIn the present study, we have used data from high throughput screens for the inhibitors of epigenetic modifiers. Computational predictive models were constructed based on the molecular descriptors. Machine learning algorithms for supervised training, Naive Bayes and Random Forest, were used to generate predictive models for the small molecule inhibitors of histone methyl-transferases and demethylases. Random forest, with the accuracy of 80%, was identified as the most accurate classifier. Further we complemented the study with substructure search approach filtering out the probable pharmacophores from the active molecules leading to drug molecules.ResultsWe show that effective use of appropriate computational algorithms could be used to learn molecular and structural correlates of biological activities of small molecules. The computational models developed could be potentially used to screen and identify potential new biological activities of molecules from large molecular libraries and prioritise them for in-depth biological assays. To the best of our knowledge, this is the first and most comprehensive computational analysis towards understanding activities of small molecules inhibitors of epigenetic modifiers.

Towards GPU-Based Common-Sense Reasoning: Using Fast Subgraph Matching

Common-sense reasoning is concerned with simulating cognitive human ability to make presumptions about the type and essence of ordinary situations encountered every day. The most popular way to represent common-sense knowledge is in the form of a semantic graph. Such type of knowledge, however, is known to be rather extensive: the more concepts added in the graph, the harder and slower it becomes to apply standard graph mining techniques. In this work, we propose a new fast subgraph matching approach to overcome these issues. Subgraph matching is the task of finding all matches of a query graph in a large data graph, which is known to be a non-deterministic polynomial time-complete problem. Many algorithms have been previously proposed to solve this problem using central processing units. Here, we present a new graphics processing unit-friendly method for common-sense subgraph matching, termed GpSense, which is designed for scalable massively parallel architectures, to enable next-generation Big Data sentiment analysis and natural language processing applications. We show that GpSense outperforms state-of-the-art algorithms and efficiently answers subgraph queries on large common-sense graphs.

Efficient and scalable labeled subgraph matching using SGMatch

Graphs are natural candidates for modeling application domains, such as social networks, pattern recognition, citation networks, or protein---protein interactions. One of the most challenging tasks in managing graphs is subgraph matching over data graphs, which attempts to find one-to-one correspondences, called solutions, among the query and data nodes. To compute solutions, most contemporary techniques use backtracking and recursion. An open research question is whether graphs can be matched based on parts and local solutions can be combined to reach a global matching. In this paper, we present an approach based on graph decomposition called SGMatch to match graphs. We represent graphs in smaller units called graphlets and develop a matching technique to leverage this representation. Pruning strategies use a new notion of edge covering called minimum hub cover and metadata, such as statistics and inverted indices, to reduce the number of matching candidates. Our evaluation of SGMatch versus contemporary algorithms, i.e., VF2, GraphQL, QuickSI, GADDI, or SPath, shows that SGMatch substantially improves the performance of current state-of-the-art techniques for larger query graphs with different structures, i.e., cliques, paths or subgraphs.

Guided Iterative Substructure Search (GI-SSS) - A New Trick for an Old Dog.

Substructure search (SSS) is a fundamental technique supported by various chemical information systems. Many users apply it in an iterative manner: they modify their queries to shape the composition of the retrieved hit sets according to their needs. We propose and evaluate two heuristic extensions of SSS aimed at simplifying these iterative query modifications by collecting additional information during query processing and visualizing this information in an intuitive way. This gives the user a convenient feedback on how certain changes to the query would affect the retrieved hit set and reduces the number of trial-and-error cycles needed to generate an optimal search result. The proposed heuristics are simple, yet surprisingly effective and can be easily added to existing SSS implementations.

Region-Based Retrieval of Remote Sensing Images Using an Unsupervised Graph-Theoretic Approach

This letter introduces a novel unsupervised graph-theoretic approach in the framework of region-based retrieval of remote sensing (RS) images. The proposed approach is characterized by two main steps: (1) modeling each image by a graph, which provides region-based image representation combining both local information and related spatial organization, and (2) retrieving the images in the archive that are most similar to the query image by evaluating graph-based similarities. In the first step, each image is initially segmented into distinct regions and then modeled by an attributed relational graph, where nodes and edges represent region characteristics and their spatial relationships, respectively. In the second step, a novel inexact graph matching strategy, which jointly exploits a subgraph isomorphism algorithm and a spectral graph embedding technique, is applied to match corresponding graphs and to retrieve images in the order of graph similarity. Experiments carried out on an archive of aerial images point out that the proposed approach significantly improves the retrieval performance compared to the state-of-the-art unsupervised RS image retrieval methods.(RS) images.

The XXL Survey

Within a cluster, gravitational effects can lead to the removal of stars from their parent galaxies. Gas hydrodynamical effects can additionally strip gas and dust from galaxies. The properties of the ICL can therefore help constrain the physical processes at work in clusters by serving as a fossil record of the interaction history. The present study is designed to characterise this ICL in a ~10^14 M_odot and z~0.53 cluster of galaxies from imaging and spectroscopic points of view. By applying a wavelet-based method to CFHT Megacam and WIRCAM images, we detect significant quantities of diffuse light. These sources were then spectroscopically characterised with MUSE. MUSE data were also used to compute redshifts of 24 cluster galaxies and search for cluster substructures. An atypically large amount of ICL has been detected in this cluster. Part of the detected diffuse light has a very weak optical stellar component and apparently consists mainly of gas emission, while other diffuse light sources are clearly dominated by old stars. Furthermore, emission lines were detected in several places of diffuse light. Our spectral analysis shows that this emission likely originates from low-excitation parameter gas. The stellar contribution to the ICL is about 2.3x10^9 yrs old even though the ICL is not currently forming a large number of stars. On the other hand, the contribution of the gas emission to the ICL in the optical is much greater than the stellar contribution in some regions, but the gas density is likely too low to form stars. These observations favour ram pressure stripping, turbulent viscous stripping, or supernovae winds as the origin of the large amount of intracluster light. Since the cluster appears not to be in a major merging phase, we conclude that ram pressure stripping is the most plausible process that generates the observed ICL sources.

Kekule.js: An Open Source JavaScript Chemoinformatics Toolkit.

Kekule.js is an open-source, object-oriented JavaScript toolkit for chemoinformatics. It provides methods for many common tasks in molecular informatics, including chemical data input/output (I/O), two- and three-dimensional (2D/3D) rendering of chemical structure, stereo identification, ring perception, structure comparison, and substructure search. Encapsulated widgets to display and edit chemical structures directly in web context are also supplied. Developed with web standards, the toolkit is ideal for building chemoinformatics applications over the Internet. Moreover, it is highly platform-independent and can also be used in desktop or mobile environments. Some initial applications, such as plugins for inputting chemical structures on the web and uses in chemistry education, have been developed based on the toolkit.

Mining the Enriched Subgraphs for Specific Vertices in a Biological Graph.

In this paper, we present a subgroup discovery method to find subgraphs in a graph that are associated with a given set of vertices. The association between a subgraph pattern and a set of vertices is defined by its significant enrichment based on a Bonferroni-corrected hypergeometric probability value. This interestingness measure requires a dedicated pruning procedure to limit the number of subgraph matches that must be calculated. The presented mining algorithm to find associated subgraph patterns in large graphs is therefore designed to efficiently traverse the search space. We demonstrate the operation of this method by applying it on three biological graph data sets and show that we can find associated subgraphs for a biologically relevant set of vertices and that the found subgraphs themselves are biologically interesting.

Quality-Aware Subgraph Matching Over Inconsistent Probabilistic Graph Databases

Resource Description Framework (RDF) has been widely used in the Semantic Web to describe resources and their relationships. The RDF graph is one of the most commonly used representations for RDF data. However, in many real applications such as the data extraction/integration, RDF graphs integrated from different data sources may often contain uncertain and inconsistent information (e.g., uncertain labels or that violate facts/rules), due to the unreliability of data sources. In this paper, we formalize the RDF data by inconsistent probabilistic RDF graphs , which contain both inconsistencies and uncertainty. With such a probabilistic graph model, we focus on an important problem, quality-aware subgraph matching over inconsistent probabilistic RDF graphs (QA-gMatch), which retrieves subgraphs from inconsistent probabilistic RDF graphs that are isomorphic to a given query graph and with high quality scores (considering both consistency and uncertainty). In order to efficiently answer QA-gMatch queries, we provide two effective pruning methods, namely adaptive label pruning and quality score pruning , which can greatly filter out false alarms of subgraphs. We also design an effective index to facilitate our proposed pruning methods, and propose an efficient approach for processing QA-gMatch queries. Finally, we demonstrate the efficiency and effectiveness of our proposed approaches through extensive experiments.

Molecular similarity guided optimization of novel Nrf2 activators with 1,2,4-oxadiazole core

DDO-7204 is a novel Nrf2 activator first identified through screening of in-house database by ARE-luciferase reporter gene assay. To further optimize this kind of Nrf2 activators efficiently, the hit-based substructure search was applied to screen the Specs database virtually. DDO-7204 contains three rings of A, B, C. SAR results showed that: for ring A, the cyclane substituent is beneficial for ARE inductivity. Enhanced flexibility of linker between ring A and ring B is not preferable for the Nrf2 activity. Ring A replaced by heterocyclic aromatic is beneficial for the Nrf2 activity. The resulting compound 7 was more potent than DDO-7204. Compound 7 can induce Nrf2 translocation into nuclear not only in HCT116 cells, but also in three normal cells such as L02, NCM460 and PC12 cells. The Nrf2-regualted genes, γ-GCS, NQO1 and HO-1, were up-regulated at a concentration-dependent manner. In addition, compound 7 showed cytoprotective effects on the three normal cells against the damage of H2O2.

Attributed graph mining in the presence of automorphism

Attributed directed graphs are directed graphs in which nodes are associated with sets of attributes. Many data from the real world can be naturally represented by this type of structure, but few algorithms are able to directly handle these complex graphs. Mining attributed graphs is a difficult task because it requires combining the exploration of the graph structure with the identification of frequent itemsets. In addition, due to the combinatorics on itemsets, subgraph isomorphisms (which have a significant impact on performances) are much more numerous than in labeled graphs. In this paper, we present a new data mining method that can extract frequent patterns from one or more directed attributed graphs. We show how to reduce the combinatorial explosion induced by subgraph isomorphisms thanks to an appropriate processing of automorphic patterns.

General Graph Data De-Anonymization

When people utilize social applications and services, their privacy suffers a potential serious threat. In this article, we present a novel, robust, and effective de-anonymization attack to mobility trace data and social data. First, we design a Unified Similarity (US) measurement, which takes account of local and global structural characteristics of data, information obtained from auxiliary data, and knowledge inherited from ongoing de-anonymization results. By analyzing the measurement on real datasets, we find that some data can potentially be de-anonymized accurately and the other can be de-anonymized in a coarse granularity. Utilizing this property, we present a US-based De-Anonymization (DA) framework, which iteratively de-anonymizes data with accuracy guarantee. Then, to de-anonymize large-scale data without knowledge of the overlap size between the anonymized data and the auxiliary data, we generalize DA to an Adaptive De-Anonymization (ADA) framework. By smartly working on two core matching subgraphs , ADA achieves high de-anonymization accuracy and reduces computational overhead. Finally, we examine the presented de-anonymization attack on three well-known mobility traces: St Andrews, Infocom06, and Smallblue, and three social datasets: ArnetMiner, Google+, and Facebook. The experimental results demonstrate that the presented de-anonymization framework is very effective and robust to noise. The source code and employed datasets are now publicly available at SecGraph [2015].

Fast processing of graph queries on a large database of small and medium-sized data graphs

We propose a new way of indexing a large database of small and medium-sized graphs and processing exact subgraph matching (or subgraph isomorphism) and approximate (full) graph matching queries. Rather than decomposing a graph into smaller units (e.g., paths, trees, graphs) for indexing purposes, we represent each graph in the database by its graph signature, which is essentially a multiset. We construct a disk-based index on all the signatures via bulk loading. During query processing, a query graph is also mapped into its signature, and this signature is searched using the index by performing multiset operations. To improve the precision of exact subgraph matching, we develop a new scheme using the concept of line graphs. Through extensive evaluation on real and synthetic graph datasets, we demonstrate that our approach provides a scalable and efficient disk-based solution for a large database of small and medium-sized graphs.

Finding a chain graph in a bipartite permutation graph

We present a polynomial-time algorithm for solving Subgraph Isomorphism where the base graphs are bipartite permutation graphs and the pattern graphs are chain graphs.

Efficient Probabilistic Supergraph Search

Given a query graph $q$ , retrieving the data graphs $g$ from a set $D$ of data graphs such that $q$ contains $g$ , namely supergraph containment search, is fundamental in graph data analysis with a wide range of real applications. It is very challenging due to the NP-Completeness of subgraph isomorphism testing. Driven by many real applications, in this paper, we study the problem of probabilistic supergraph search; that is, given a set $D$ of uncertain data graphs, a certain query graph $q$ and a probability threshold $\theta$ , we retrieve the data graphs $g^{u}$ from $D$ such that the probability of $q$ containing $g^{u}$ is not smaller than $\theta$ . We show that besides the NP-Completeness of subgraph isomorphism testing, the problem of calculating probabilities is #P-Complete; thus, it is even more challenging than the supergraph containment search. To tackle the computational hardness, we first propose two novel pruning rules, based on probabilistic connectivity and features, respectively, to efficiently prune non-promising data graphs. Then, efficient verification algorithms are developed with the aim of sharing computation and terminating non-promising computation as early as possible. Extensive performance studies on both real and synthetic data demonstrate the efficiency and effectiveness of our techniques in practice.

Effective Frequent Subgraph Extraction applying the Weighted Graph in Big Data

Mining big data has become an important problem in the graph pattern mining research area. Inorder to select useful features, recent graph mining techniques applies repeated mining of frequent subgraphs either by varying minimum supports or by dividing a graph database recursively. Frequent subgraph mining is an import task for exploratory data analysis on graph database. Frequent subgraph mining entails two significant overheads. It is concerted with candidate set generation and isomorphism checking. Finding subgraph isomorphism is an important problem in many applications which deal with data modeled asgraphs. In this work, we propose to reduced the search space and address isomorphism overheads, a weighted approach to subgraph mining. The objective of this work is to investigate the benefits that the concept of weighted frequent subgraph mining can offer in the context of the graph model based classification. Weighted subgraphs are graps where some of the vertexes or edges are considered to be more significant than others. Received (January 12, 2016), Review Result (January 26, 2016) Accepted (February 2, 2016), Published (March 31, 2016) 430-742 Division of Computer Engineering, SungKyul Univ., Gyeonggi-do, Anyong-si, Korea email: jkcho@sungkyul.ac.kr Effective Frequent Subgraph Extraction applying the Weighted Graph in Big Data Copyright c 2016 HSST 62