Similarity Search Strategy Research Articles

Mycielski Based 2d-Predictive Image Coding Algorithm

The Mycielski method is a prospering prediction algorithm which is based on searching and finding largest repeated binary patterns. It uses infinite-past data to devise a rule based prediction method on a time series. In this work, a novel two-dimensional (image processing) version of the Mycielski algorithm is proposed. Since the dimensionality definition of “past” data increases in two-dimensional signals, the proposed algorithm also needs to handle how the boundaries of the pixel cliques are iteratively extended in the neighborhood of a current pixel. The clique extension invokes novel similarity search strategies that depend on the chosen physical distance metric. The proposed prediction algorithm is used for predictive image compression and performance comparisons with other predictive coding methods are presented.

Similarity Search over the Cloud Based on Image Descriptors' Dimensions Value Cardinalities

In recognition that in modern applications billions of images are stored into distributed databases in different logical or physical locations, we propose a similarity search strategy over the cloud based on the dimensions value cardinalities of image descriptors. Our strategy has low preprocessing requirements by dividing the computational cost of the preprocessing steps into several nodes over the cloud and locating the descriptors with similar dimensions value cardinalities logically close. New images are inserted into the distributed databases over the cloud efficiently, by supporting dynamical update in real-time. The proposed insertion algorithm has low computational complexity, depending exclusively on the dimensionality of descriptors and a small subset of descriptors with similar dimensions value cardinalities. Finally, an efficient query processing algorithm is proposed, where the dimensions of image descriptors are prioritized in the searching strategy, assuming that dimensions of high value cardinalities have more discriminative power than the dimensions of low ones. The computation effort of the query processing algorithm is divided into several nodes over the cloud infrastructure. In our experiments with seven publicly available datasets of image descriptors, we show that the proposed similarity search strategy outperforms competitive methods of single node, parallel and cloud-based architectures, in terms of preprocessing cost, search time and accuracy.

A new method to improve network topological similarity search: applied to fold recognition.

Similarity search is the foundation of bioinformatics. It plays a key role in establishing structural, functional and evolutionary relationships between biological sequences. Although the power of the similarity search has increased steadily in recent years, a high percentage of sequences remain uncharacterized in the protein universe. Thus, new similarity search strategies are needed to efficiently and reliably infer the structure and function of new sequences. The existing paradigm for studying protein sequence, structure, function and evolution has been established based on the assumption that the protein universe is discrete and hierarchical. Cumulative evidence suggests that the protein universe is continuous. As a result, conventional sequence homology search methods may be not able to detect novel structural, functional and evolutionary relationships between proteins from weak and noisy sequence signals. To overcome the limitations in existing similarity search methods, we propose a new algorithmic framework-Enrichment of Network Topological Similarity (ENTS)-to improve the performance of large scale similarity searches in bioinformatics. We apply ENTS to a challenging unsolved problem: protein fold recognition. Our rigorous benchmark studies demonstrate that ENTS considerably outperforms state-of-the-art methods. As the concept of ENTS can be applied to any similarity metric, it may provide a general framework for similarity search on any set of biological entities, given their representation as a network. Source code freely available upon request : lxie@iscb.org.

The influence of image descriptors’ dimensions’ value cardinalities on large-scale similarity search

In this empirical study, we evaluate the impact of the dimensions’ value cardinality (DVC) of image descriptors in each dimension, on the performance of large-scale similarity search. DVCs are inherent characteristics of image descriptors defined for each dimension as the number of distinct values of image descriptors, thus expressing the dimension’s discriminative power. In our experiments, with six publicly available datasets of image descriptors of different dimensionality (64–5,000 dim) and size (240 K–1 M), (a) we show that DVC varies, due to the existence of several extraction methods using different quantization and normalization techniques; (b) we also show that image descriptor extraction strategies tend to follow the same DVC distribution function family; therefore, similarity search strategies can exploit image descriptors DVCs, irrespective of the sizes of the datasets; (c) based on a canonical correlation analysis, we demonstrate that there is a significant impact of image descriptors’ DVCs on the performance of the baseline LSH method [8] and three state-of-the-art hashing methods: SKLSH [28], PCA-ITQ [10], SPH [12], as well as on the performance of MSIDX method [34], which exploits the DVC information; (d) we experimentally demonstrate the influence of DVCs in both the sequential search and in the aforementioned similarity search methods and discuss the advantages of our findings. We hope that our work will motivate researchers for considering DVC analysis as a tool for the design of similarity search strategies in image databases.

Template matching using an improved electromagnetism-like algorithm

Template matching (TM) plays an important role in several image-processing applications such as feature tracking, object recognition, stereo matching, and remote sensing. The TM approach seeks for the best-possible resemblance between a subimage known as template and its coincident region within a source image. TM involves two critical aspects: similarity measurement and search strategy. The simplest available TM method aims for the best-possible coincidence between the images through an exhaustive computation of the normalized cross-correlation (NCC) values (similarity measurement) for all elements of the source image (search strategy). Recently, several TM algorithms that are based on evolutionary approaches have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the electromagnetism-like algorithm (EMO) is proposed to reduce the number of search locations in the TM process. The algorithm uses an enhanced EMO version, which incorporates a modification of the local search procedure to accelerate the exploitation process. As a result, the new EMO algorithm can substantially reduce the number of fitness function evaluations while preserving the good search capabilities of the original EMO. In the proposed approach, particles represent search locations, which move throughout the positions of the source image. The NCC coefficient, considered as the fitness value (charge extent), evaluates the matching quality presented between the template image and the coincident region of the source image, for a determined search position (particle). The number of NCC evaluations is also reduced by considering a memory, which stores the NCC values previously visited to avoid the re-evaluation of the same search locations (particles). Guided by the fitness values (NCC coefficients), the set of candidate positions are evolved through EMO operators until the best-possible resemblance is determined. The conducted simulations show that the proposed method achieves the best balance over other TM algorithms in terms of estimation accuracy and computational cost.

A Comparison of Three Library and Information Science Databases

The focus of this study was to compare Library, Information Science & Technology s (LISTA), Library Literature & Information Science Full-Text Articles (LLISFT), and Information Science & Technology s (ISTA) scholarly databases. Five different search strategies were used to identify differences among the three library and information science (LIS) databases’ coverage of selected topics and the degree of overlap in the coverage of those topics. This study used similar search strategies as a previous LIS database comparison study by Read and Smith (2000) so that results over time selected topics among the three LIS databases. While LISTA has the widest range of subject coverage, ISTA has greater strength of coverage of technology-related topics, and LLISFT has greater strength of coverage in more traditional library-related topics. Since LISTA has the broadest range of coverage, it would be a logical choice for libraries that can only afford a subscription to one LIS database. Libraries that support LIS degree program should consider subscription to all three databases to ensure coverage of technical topics as well as more traditional topics related to library programs and services.

A weighted sampling algorithm for the design of RNA sequences with targeted secondary structure and nucleotide distribution

Motivations: The design of RNA sequences folding into predefined secondary structures is a milestone for many synthetic biology and gene therapy studies. Most of the current software uses similar local search strategies (i.e. a random seed is progressively adapted to acquire the desired folding properties) and more importantly do not allow the user to control explicitly the nucleotide distribution such as the GC-content in their sequences. However, the latter is an important criterion for large-scale applications as it could presumably be used to design sequences with better transcription rates and/or structural plasticity.Results: In this article, we introduce IncaRNAtion, a novel algorithm to design RNA sequences folding into target secondary structures with a predefined nucleotide distribution. IncaRNAtion uses a global sampling approach and weighted sampling techniques. We show that our approach is fast (i.e. running time comparable or better than local search methods), seedless (we remove the bias of the seed in local search heuristics) and successfully generates high-quality sequences (i.e. thermodynamically stable) for any GC-content. To complete this study, we develop a hybrid method combining our global sampling approach with local search strategies. Remarkably, our glocal methodology overcomes both local and global approaches for sampling sequences with a specific GC-content and target structure.Availability: IncaRNAtion is available at csb.cs.mcgill.ca/incarnation/Contact: jeromew@cs.mcgill.ca or yann.ponty@lix.polytechnique.frSupplementary Information: Supplementary data are available at Bioinformatics online.

A novel evolutionary algorithm inspired by the states of matter for template matching

Template matching (TM) plays an important role in several image processing applications such as feature tracking, object recognition, stereo matching and remote sensing. The TM approach seeks the best possible resemblance between a sub-image, known as template, and its coincident region within a source image. TM has two critical aspects: similarity measurement and search strategy. The simplest available TM method finds the best possible coincidence between the images through an exhaustive computation of the Normalized Cross-Correlation (NCC) value (similarity measurement) for all elements in the source image (search strategy). Unfortunately, the use of such approach is strongly restricted since the NCC evaluation is a computationally expensive operation. Recently, several TM algorithms that are based on evolutionary approaches, have been proposed to reduce the number of NCC operations by calculating only a subset of search locations. In this paper, a new algorithm based on the states of matter phenomenon is proposed to reduce the number of search locations in the TM process. In the proposed approach, individuals emulate molecules that experiment state transitions which represent different exploration–exploitation levels. In the algorithm, the computation of search locations is drastically reduced by incorporating a fitness calculation strategy which indicates when it is feasible to calculate or to only estimate the NCC value for new search locations. Conducted simulations show that the proposed method achieves the best balance in comparison to other TM algorithms considering the estimation accuracy and the computational cost.

Construction of Customized Sub-Databases from NCBI-nr Database for Rapid Annotation of Huge Metagenomic Datasets Using a Combined BLAST and MEGAN Approach

We developed a fast method to construct local sub-databases from the NCBI-nr database for the quick similarity search and annotation of huge metagenomic datasets based on BLAST-MEGAN approach. A three-step sub-database annotation pipeline (SAP) was further proposed to conduct the annotation in a much more time-efficient way which required far less computational capacity than the direct NCBI-nr database BLAST-MEGAN approach. The 1st BLAST of SAP was conducted using the original metagenomic dataset against the constructed sub-database for a quick screening of candidate target sequences. Then, the candidate target sequences identified in the 1st BLAST were subjected to the 2nd BLAST against the whole NCBI-nr database. The BLAST results were finally annotated using MEGAN to filter out those mistakenly selected sequences in the 1st BLAST to guarantee the accuracy of the results. Based on the tests conducted in this study, SAP achieved a speedup of ∼150–385 times at the BLAST e-value of 1e–5, compared to the direct BLAST against NCBI-nr database. The annotation results of SAP are exactly in agreement with those of the direct NCBI-nr database BLAST-MEGAN approach, which is very time-consuming and computationally intensive. Selecting rigorous thresholds (e.g. e-value of 1e–10) would further accelerate SAP process. The SAP pipeline may also be coupled with novel similarity search tools (e.g. RAPsearch) other than BLAST to achieve even faster annotation of huge metagenomic datasets. Above all, this sub-database construction method and SAP pipeline provides a new time-efficient and convenient annotation similarity search strategy for laboratories without access to high performance computing facilities. SAP also offers a solution to high performance computing facilities for the processing of more similarity search tasks.

Swimming into peptidomimetic chemical space using pepMMsMIMIC

pepMMsMIMIC is a novel web-oriented peptidomimetic compound virtual screening tool based on a multi-conformers three-dimensional (3D)-similarity search strategy. Key to the development of pepMMsMIMIC has been the creation of a library of 17 million conformers calculated from 3.9 million commercially available chemicals collected in the MMsINC® database. Using as input the 3D structure of a peptide bound to a protein, pepMMsMIMIC suggests which chemical structures are able to mimic the protein–protein recognition of this natural peptide using both pharmacophore and shape similarity techniques. We hope that the accessibility of pepMMsMIMIC (freely available at http://mms.dsfarm.unipd.it/pepMMsMIMIC) will encourage medicinal chemists to de-peptidize protein–protein recognition processes of biological interest, thus increasing the potential of in silico peptidomimetic compound screening of known small molecules to expedite drug development.

LHC searches for non-chiral weakly charged multiplets

In this paper we consider vector representations of fermions in multiplets of SU(2) L with a lightest neutral state, a notable example of which is the wino LSP in anomaly-mediated models. Because of the expected small one-loop-level splitting between charged and neutral states, the path length in the detector is finite but short so the signature is distinctive but challenging. Our analysis determines the LHC reach of models with additional weakly charged vector-like matter using similar search strategies to existing studies of some specific models. Currently planned search strategies would fail to find such particles, although early LHC data could be used to better understand our signal and possible backgrounds.

Impact of early psychosocial factors (childhood socioeconomic factors and adversities) on future risk of type 2 diabetes, metabolic disturbances and obesity: a systematic review

BackgroundPsychological factors and socioeconomic status (SES) have a notable impact on health disparities, including type 2 diabetes risk. However, the link between childhood psychosocial factors, such as childhood adversities or parental SES, and metabolic disturbances is less well established. In addition, the lifetime perspective including adult socioeconomic factors remains of further interest.We carried out a systematic review with the main question if there is evidence in population- or community-based studies that childhood adversities (like neglect, traumata and deprivation) have considerable impact on type 2 diabetes incidence and other metabolic disturbances. Also, parental SES was included in the search as risk factor for both, diabetes and adverse childhood experiences. Finally, we assumed that obesity might be a mediator for the association of childhood adversities with diabetes incidence. Therefore, we carried out a second review on obesity, applying a similar search strategy.MethodsTwo systematic reviews were carried out. Longitudinal, population- or community-based studies were included if they contained data on psychosocial factors in childhood and either diabetes incidence or obesity risk.ResultsWe included ten studies comprising a total of 200,381 individuals. Eight out of ten studies indicated that low parental status was associated with type 2 diabetes incidence or the development of metabolic abnormalities. Adjustment for adult SES and obesity tended to attenuate the childhood SES-attributable risk but the association remained. For obesity, eleven studies were included with a total sample size of 70,420 participants. Four out of eleven studies observed an independent association of low childhood SES on the risk for overweight and obesity later in life.ConclusionsTaken together, there is evidence that childhood SES is associated with type 2 diabetes and obesity in later life. The database on the role of psychological factors such as traumata and childhood adversities for the future risk of type 2 diabetes or obesity is too small to draw conclusions. Thus, more population-based longitudinal studies and international standards to assess psychosocial factors are needed to clarify the mechanisms leading to the observed health disparities.

Shannon Entropy-Based Fingerprint Similarity Search Strategy

For fingerprint searching using multiple active reference compounds, an information entropy-based similarity method is introduced as an alternative to conventional similarity coefficients and search strategies. The approach involves the determination of the fingerprint bit pattern entropy of a compound reference set and recalculation of the entropy following the addition of individual test compounds. If a database compound shares similar bit patterns with reference set molecules, adding this compound to the reference set only produces a small change in system entropy. By contrast, inclusion of a compound having a dissimilar fingerprint leads to a notable increase in entropy. Thus, database compounds can be screened for candidate molecules that do not cause significant changes in reference set fingerprint entropy. Compared to nearest neighbor methods, this approach has the computational advantage that it extracts reference set information only once prior to similarity searching. Test calculations on different compound data sets, fingerprints, and screening databases reveal that the ability of our entropy-based method to detect active compounds is often superior to data fusion techniques and Tanimoto similarity calculations.

Relevance of Feature Combinations for Similarity Searching Using General or Activity Class-Directed Molecular Fingerprints

We introduce a methodology for the systematic identification of feature combinations derived from fingerprints of bioactive compounds. Structural features were organized in co-occurrence networks from which reference set-based feature cliques were extracted. A similarity search strategy is presented that is based on frequency ranking of cliques. Three types of fingerprints have been compared that are either general in their design or incorporate, to a different degree, compound class information. Taking control calculations into account, search performance was overall best for a molecule-specific extended connectivity fingerprint. For compound class-directed fingerprints, reference set-derived feature cliques occurring with low frequency in a screening database were found to consistently enrich active compounds in database selection sets, even if the features are not highly conserved among reference compounds. Thus, in contrast to general fingerprints, feature combinations play a crucial role in similarity searching using activity-class-directed fingerprints.

An Insight into Forensic Document Examiner Expertise for Discriminating Between Forged and Disguised Signatures

It has previously been shown that forensic document examiners (FDEs) have expertise in providing opinions about whether questioned signatures are genuine or simulated. This study extends the exploration of FDE expertise by evaluating the performance of eight FDEs and 12 control subjects at identifying signatures as either forgeries or the disguised writing of a specimen provider. Subject eye movements and response times were recorded with a Tobii 1750 eye tracker during the signature evaluations. Using a penalty scoring system, FDEs performed significantly better than control subjects (t = 2.465, p = 0.024), with one FDE able to correctly call 13 of the 16 test stimuli (and three inconclusive calls). An analysis of eye movement search patterns by the subjects indicated that a very similar search strategy was employed by both groups, suggesting that visual inspection of signatures is mediated by a bottom up search strategy. However, FDEs spent greater than 50% longer to make a decision than the control group. The findings are suggestive that for some stimuli FDEs can discriminate between forgeries and disguises, and that this ability is due to a careful inspection and consideration of multiple features within a signature.

Fisher: a program for the detection of H/ACA snoRNAs using MFE secondary structure prediction and comparative genomics – assessment and update

BackgroundThe H/ACA family of small nucleolar RNAs (snoRNAs) plays a central role in guiding the pseudouridylation of ribosomal RNA (rRNA). In an effort to systematically identify the complete set of rRNA-modifying H/ACA snoRNAs from the genome sequence of the budding yeast, Saccharomyces cerevisiae, we developed a program – Fisher – and previously presented several candidate snoRNAs based on our analysis [1].FindingsIn this report, we provide a brief update of this work, which was aborted after the publication of experimentally-identified snoRNAs [2] identical to candidates we had identified bioinformatically using Fisher. Our motivation for revisiting this work is to report on the status of the candidate snoRNAs described in [1], and secondly, to report that a modified version of Fisher together with the available multiple yeast genome sequences was able to correctly identify several H/ACA snoRNAs for modification sites not identified by the snoGPS program [3]. While we are no longer developing Fisher, we briefly consider the merits of the Fisher algorithm relative to snoGPS, which may be of use for workers considering pursuing a similar search strategy for the identification of small RNAs. The modified source code for Fisher is made available as supplementary material.ConclusionOur results confirm the validity of using minimum free energy (MFE) secondary structure prediction to guide comparative genomic screening for RNA families with few sequence constraints.

Cytotoxin-associated gene-A-seropositive virulent strains of Helicobacter pylori and atherosclerotic diseases: a systematic review

A systematic meta-analysis was performed to explore the role of cytotoxin-associated gene-A (CagA) seropositive strains of Helicobacter pylori (H. pylori) in the pathogenesis of atherosclerotic diseases. Data sources Data from Medline, EMBASE, CBMdisc, CNKI and the Cochrane Collaboration database were searched. Similar search strategies were applied to each of these databases. Study selection The review was restricted to the case-control studies on infective, chronic virulent CagA strains of H. pylori, involving the risk of ischemic stroke and coronary heart disease, ineligible studies were excluded. Two reviewers independently extracted the data and assessed study quality. Totally 26 case-control studies (11 studies on ischemic stroke and 15 studies on coronary heart disease) were retrieved and considered. The combined data revealed that the chronic seropositive virulent strains of H. pylori infection had a trend of increasing the risk of ischemic strokes and coronary heart diseases, yielding pooled ORs of 2.68 (95% CI: 2.20, 3.27) and 2.11 (95% CI: 1.70, 2.62), respectively. We also performed subgroup analyses, dividing the total population into Caucasian and Chinese subgroups. Through the subgroup analysis, no significant difference was found between the subgroups. Our results support the hypothesis that CagA-seropositive strains infection is significantly associated with susceptibility to ischemic strokes and coronary heart diseases. The magnitude of the association with atherosclerotic diseases needs to be confirmed by prospective studies and the studies on CagA-seropositive strains eradication are more important.

Query-Adaptive Fusion for Multimodal Search

We conduct a broad survey of query-adaptive search strategies in a variety of application domains, where the internal retrieval mechanisms used for search are adapted in response to the anticipated needs for each individual query experienced by the system. While these query-adaptive approaches can range from meta-search over text collections to multimodal search over video databases, we propose that all such systems can be framed and discussed in the context of a single, unified framework. In our paper, we keep an eye towards the domain of video search, where search cues are available from a rich set of modalities, including textual speech transcripts, low-level visual features, and high-level semantic concept detectors. The relative efficacy of each of the modalities is highly variant between many types of queries. We observe that the state of the art in query-adaptive retrieval frameworks for video collections is highly dependent upon the definition of classes of queries, which are groups of queries that share similar optimal search strategies, while many applications in text and Web retrieval have included many advanced strategies, such as direct prediction of search method performance and inclusion of contextual cues from the searcher. We conclude that such advanced strategies previously developed for text retrieval have a broad range of possible applications in future research in multimodal video search.

A New Colorization Algorithm Based on Dynamic Neighbor-Relevant Chromatic Channel Information

This paper is concerned with the problem of automatic colorization of grayscale images via color transfer. Some deficiencies of Welsh's colorization algorithm are pointed out and analyzed. Then, a new colorization algorithm is proposed, which uses a new similarity function and a new search strategy for pixel matching. The new function incorporates both the luminance information and chromatic values of previously colorized neighboring pixels. The intrinsic space clustering property of an image is taken into account in the strategy by using Hilbert filling curve scanning order. Experimental results showed that the proposed algorithm outperforms Welsh's algorithm in both colorization effect and computational complexity. Moreover, the proposed similarity function and search strategy can easily be embedded into other colorization algorithms to improve their performance.

Searching strategy of the painted turtle Chrysemys picta across spatial scales

The movement path made by an organism in search of a resource contributes strongly to the success of that search. But are similar search strategies used at very different spatial scales? Using painted turtles as our model organism, we ask (1) whether terrestrial search paths, both shape and rate, are similar at small (30 m) and large (>500 m) spatial scales, (2) whether those search paths are linear, and (3) what environmental conditions affect the shape of the search path? We found that movement paths at both small and large spatial scales were nearly straight and statistically indistinguishable from one another. The mean turning angles at small and large spatial scales were statistically similar and were best represented by an angle of 1.8°. Net displacement rates were, however, quite different between small- and large-scale paths. At small spatial scales, path shape and movement rate were most strongly affected by cloud cover and turtle size, respectively. Our results indicate that the shape of a search path is consistent over small and large spatial scales and gives support for extrapolating larger-scale path shape from small-scale observations. Net displacement rates, however, were scale dependent and thus caution must be taken in using behaviours detected at small scales to predict movement rates over larger areas. An unexpected result was that painted turtles did not follow drainages in search of aquatic habitat in this study system.