Detection Of Structural Similarities Research Articles

Algorithm of web page similarity comparison based on visual block

Phishing often deceives users due to the relative similarity to the true pages on a layout and leads to considerable losses for the society. Consequently, detecting phishing sites has been an urgent activity. By researching phishing web pages using web page screenshots, we discover that this kind of web pages use numerous web page screenshots to achieve the close similarity to the true page and avoid the text and structure similarity detection. This study introduces a new similarity matching algorithm based on visual blocks. First, the RenderLayer tree of the web page is obtained to extract the visual block. Second, an algorithm that will settle the jumbled visual blocks, including the deletion of the small visual blocks and the emergence of the overlapping visual blocks, is designed. Finally, the similarity between the two web pages is assessed. The proposed algorithm sets different thresholds to achieve the optimal missing and false alarm rates.

Deer and Horses in Atlantic Rock Art: A Structural Analysis of Iconography in the Landscape

The aim of this paper is to show how the structural analysis of the iconography and the landscape can make feasible an understanding of the meaning of some rock engravings. The distribution of several zones with hunting scenes in Atlantic rock art are analyzed, finding some constants in the distribution of some landscape characteristics. This distribution is observed on several sites and at various scales. The detection of structural similarities at several sites occupied in Late Prehistory by communities of the same ethnic group can be explained by the existence of a ritual developed in these rock art areas. This ritual could be similar to the “curros”, an old tradition related to wild horses. It is impossible to know if the curros are the survival of an ancient tradition, but it is possible to use the study of that tradition to understand, at least at a functional level, what is being represented on the panels with quadrupeds in Atlantic rock art.

An NLP-based cross-document approach to narrative structure discovery

Structural similarities across narratives play an important role in many areas of humanities research. In this article, we describe a methodology and an implementation to uncover such similarities automatically in two application scenarios. In both scenarios—ritual and folktale studies—existing research examines similarities of narratives on a structural level and discusses structural principles that govern the combination of individual events to tales or rituals. We present a largely unsupervised and fully automated alignment-based approach for the detection of structural similarities of narratives that allows for data-driven quantitative studies of narrative structure. Our approach makes use of an adaptable, computational linguistic processing architecture that creates integrated discourse representations of events, participants, and their relations. Our contributions are twofold and crucially build on the automatically constructed discourse representations: (1) We examine different ‘semantics-driven cross-document alignment’ algorithms that determine (sequences of) events shared between narratives, to support the search for recurrent elements in their structure. The alignment algorithms are evaluated in two experiments. (2) We develop ‘tools for exploration and interpretation’ that we offer to humanities researchers for investigation of the analyzed data. These include search facilities, visualizations, statistical overviews, and a graph-based algorithm that identifies densely aligned regions across documents for targeted inspection.

3D-Hit: fast structural comparison of proteins on multicore architectures

3D-Hit is a well established method for rapid detection of structural similarities between proteins, which is widely used in various bioinformatics web servers (MetaServer, GRDB, 3D-Fun, Rosetta, etc.). The algorithm decomposes proteins into set of overlaping segments of 9–13 residues, then tries to match them using root mean square distance metric. The best aligned pairs of segments are selected as seeds for futher analysis. Those initial hits are expanded by iterative process in order to construct the global structural alignment by concatenating pairs of matching segments. The method has the same accuracy as the other state-of-the-art structural comparison algorithms (LGscore2, DALI), yet it provides much faster processing times, and can be used in a high-throughput setup as the structural module of bioinformatics pipelines. The method is optimized in terms of speed and accuracy to work on novel computer architectures, such as PowerXCell8i and Sun Constellation System. Here, we provide the source code of the 3D-Hit program, describe selected architectures on which the software was ported, present programing models, point out significant porting steps and sumarize performance comparisons.

Fast alignment and comparison of RNA structures

To recognize remote relationships between RNA molecules, one must be able to align structures without regard to sequence similarity. We have implemented a method, which is swift [O(n(2))], sensitive and tolerant of large gaps and insertions. Molecules are broken into overlapping fragments, which are characterized by their memberships in a probabilistic classification based on local geometry and H-bonding descriptors. This leads to a probabilistic similarity measure that is used in a conventional dynamic programming method. Examples are given of database searching, the detection of structural similarities, which would not be found using sequence based methods, and comparisons with a previously published approach. Source code (C and perl) and binaries for linux are freely available at www.zbh.uni-hamburg.de/fries.

Malware classification based on call graph clustering

Each day, anti-virus companies receive tens of thousands samples of potentially harmful executables. Many of the malicious samples are variations of previously encountered malware, created by their authors to evade pattern-based detection. Dealing with these large amounts of data requires robust, automatic detection approaches. This paper studies malware classification based on call graph clustering. By representing malware samples as call graphs, it is possible to abstract certain variations away, enabling the detection of structural similarities between samples. The ability to cluster similar samples together will make more generic detection techniques possible, thereby targeting the commonalities of the samples within a cluster. To compare call graphs mutually, we compute pairwise graph similarity scores via graph matchings which approximately minimize the graph edit distance. Next, to facilitate the discovery of similar malware samples, we employ several clustering algorithms, including k-medoids and Density-Based Spatial Clustering of Applications with Noise (DBSCAN). Clustering experiments are conducted on a collection of real malware samples, and the results are evaluated against manual classifications provided by human malware analysts. Experiments show that it is indeed possible to accurately detect malware families via call graph clustering. We anticipate that in the future, call graphs can be used to analyse the emergence of new malware families, and ultimately to automate implementation of generic detection schemes.

Fast overlapping of protein contact maps by alignment of eigenvectors

Searching for structural similarity is a key issue of protein functional annotation. The maximum contact map overlap (CMO) is one of the possible measures of protein structure similarity. Exact and approximate methods known to optimize the CMO are computationally expensive and this hampers their applicability to large-scale comparison of protein structures. In this article, we describe a heuristic algorithm (Al-Eigen) for finding a solution to the CMO problem. Our approach relies on the approximation of contact maps by eigendecomposition. We obtain good overlaps of two contact maps by computing the optimal global alignment of few principal eigenvectors. Our algorithm is simple, fast and its running time is independent of the amount of contacts in the map. Experimental testing indicates that the algorithm is comparable to exact CMO methods in terms of the overlap quality, to structural alignment methods in terms of structure similarity detection and it is fast enough to be suited for large-scale comparison of protein structures. Furthermore, our preliminary tests indicates that it is quite robust to noise, which makes it suitable for structural similarity detection also for noisy and incomplete contact maps. Available at http://bioinformatics.cs.unibo.it/Al-Eigen.

Reduced representation of protein structure: implications on efficiency and scope of detection of structural similarity

BackgroundComputational comparison of two protein structures is the starting point of many methods that build on existing knowledge, such as structure modeling (including modeling of protein complexes and conformational changes), molecular replacement, or annotation by structural similarity. In a commonly used strategy, significant effort is invested in matching two sets of atoms. In a complementary approach, a global descriptor is assigned to the overall structure, thus losing track of the substructures within.ResultsUsing a small set of geometric features, we define a reduced representation of protein structure, together with an optimizing function for matching two representations, to provide a pre-filtering stage in a database search. We show that, in a straightforward implementation, the representation performs well in terms of resolution in the space of protein structures, and its ability to make new predictions.ConclusionsPerhaps unexpectedly, a substantial discriminating power already exists at the level of main features of protein structure, such as directions of secondary structural elements, possibly constrained by their sequential order. This can be used toward efficient comparison of protein (sub)structures, allowing for various degrees of conformational flexibility within the compared pair, which in turn can be used for modeling by homology of protein structure and dynamics.

Protein structure mining using a structural alphabet

We present a comprehensive evaluation of a new structure mining method called PB-ALIGN. It is based on the encoding of protein structure as 1D sequence of a combination of 16 short structural motifs or protein blocks (PBs). PBs are short motifs capable of representing most of the local structural features of a protein backbone. Using derived PB substitution matrix and simple dynamic programming algorithm, PB sequences are aligned the same way amino acid sequences to yield structure alignment. PBs are short motifs capable of representing most of the local structural features of a protein backbone. Alignment of these local features as sequence of symbols enables fast detection of structural similarities between two proteins. Ability of the method to characterize and align regions beyond regular secondary structures, for example, N and C caps of helix and loops connecting regular structures, puts it a step ahead of existing methods, which strongly rely on secondary structure elements. PB-ALIGN achieved efficiency of 85% in extracting true fold from a large database of 7259 SCOP domains and was successful in 82% cases to identify true super-family members. On comparison to 13 existing structure comparison/mining methods, PB-ALIGN emerged as the best on general ability test dataset and was at par with methods like YAKUSA and CE on nontrivial test dataset. Furthermore, the proposed method performed well when compared to flexible structure alignment method like FATCAT and outperforms in processing speed (less than 45 s per database scan). This work also establishes a reliable cut-off value for the demarcation of similar folds. It finally shows that global alignment scores of unrelated structures using PBs follow an extreme value distribution. PB-ALIGN is freely available on web server called Protein Block Expert (PBE) at http://bioinformatics.univ-reunion.fr/PBE/.

Exploiting structural similarity for effective Web information extraction

In this paper, we propose a classification technique for Web pages, based on the detection of structural similarities among semistructured documents, and devise an architecture exploiting such technique for the purpose of information extraction. The proposal significantly differs from standard methods based on graph-matching algorithms, and is based on the idea of representing the structure of a document as a time series in which each occurrence of a tag corresponds to an impulse. The degree of similarity between documents is then stated by analyzing the frequencies of the corresponding Fourier transform. Experiments on real data show the effectiveness of the proposed technique.

Prediction of protein function from protein sequence and structure.

The sequence of a genome contains the plans of the possible life of an organism, but implementation of genetic information depends on the functions of the proteins and nucleic acids that it encodes. Many individual proteins of known sequence and structure present challenges to the understanding of their function. In particular, a number of genes responsible for diseases have been identified but their specific functions are unknown. Whole-genome sequencing projects are a major source of proteins of unknown function. Annotation of a genome involves assignment of functions to gene products, in most cases on the basis of amino-acid sequence alone. 3D structure can aid the assignment of function, motivating the challenge of structural genomics projects to make structural information available for novel uncharacterized proteins. Structure-based identification of homologues often succeeds where sequence-alone-based methods fail, because in many cases evolution retains the folding pattern long after sequence similarity becomes undetectable. Nevertheless, prediction of protein function from sequence and structure is a difficult problem, because homologous proteins often have different functions. Many methods of function prediction rely on identifying similarity in sequence and/or structure between a protein of unknown function and one or more well-understood proteins. Alternative methods include inferring conservation patterns in members of a functionally uncharacterized family for which many sequences and structures are known. However, these inferences are tenuous. Such methods provide reasonable guesses at function, but are far from foolproof. It is therefore fortunate that the development of whole-organism approaches and comparative genomics permits other approaches to function prediction when the data are available. These include the use of protein-protein interaction patterns, and correlations between occurrences of related proteins in different organisms, as indicators of functional properties. Even if it is possible to ascribe a particular function to a gene product, the protein may have multiple functions. A fundamental problem is that function is in many cases an ill-defined concept. In this article we review the state of the art in function prediction and describe some of the underlying difficulties and successes.

On the Evolution of Protein Folds: Are Similar Motifs in Different Protein Folds the Result of Convergence, Insertion, or Relics of an Ancient Peptide World?

Thispaper presents and discusses evidence suggesting how the diversity of domain folds in existence today might have evolved from peptide ancestors. We apply a structure similarity detection method to detect instances where localized regions of different protein folds contain highly similar sequences and structures. Results of performing an all-on-all comparison of known structures are described and compared with other recently published findings. The numerous instances of local sequence and structure similarities within different protein folds, together with evidence from proteins containing sequence and structure repeats, argues in favor of the evolution of modern single polypeptide domains from ancient short peptide ancestors (antecedent domain segments (ADSs)). In this model, ancient protein structures were formed by self-assembling aggregates of short polypeptides. Subsequently, and perhaps concomitantly with the evolution of higher fidelity DNA replication and repair systems, single polypeptide domains arose from the fusion of ADSs genes. Thus modern protein domains may have a polyphyletic origin.

Automatic detection of structural similarities among chemical compounds

A general technique is described for the automatic determination of structural similarity among pairs of chemical compounds, and applications of this technique in the computer-based manipulation of chemical information are discussed.