Similarity Search Approach Research Articles

Similarity searching for wafer bin maps by measuring shape, location, and size similarities of defect patterns

A wafer bin map (WBM) is a visual representation of the spatial distribution of defective chips on a wafer. WBMs showing specific defect patterns are usually a result of process-assignable causes; thus, it is important to identify them to eliminate assignable causes. With advances in semiconductor manufacturing technology, identifying new defect patterns, and diagnosing their causes have become critical. However, most existing methods for WBM analysis use a supervised learning approach, which only detects previously known defect patterns. The similarity-search approach is a suitable alternative for defining new defect patterns. The proposed method uses an unsupervised approach to search for similar WBMs by measuring the similarities of three spatial features of defect patterns–shape, location, and size–which are useful for defining new defect patterns and diagnosing their causes. These three similarities are achieved using tensor voting and the mountain function for shape similarity, Euclidean distance for location similarity, and a combination of defect count and average radius for size similarity. The overall similarity was assessed using the weighted average of the three similarities. The weights are determined by quantifying the uncertainty of each similarity based on information entropy theory to better distinguish between similar patterns. The experimental results demonstrate the effectiveness of the proposed method compared to existing methods and highlight its capability to identify and describe the spatial features of defect patterns.

CANTATAdb 3.0: An Updated Repository of Plant Long Non-Coding RNAs.

CANTATAdb 3.0 is an updated database of plant long non-coding RNAs (lncRNAs), containing 571,688 lncRNAs identified across 108 species, including 100 Magnoliopsida (flowering plants), a significant expansion from the previous version. A notable feature is the inclusion of 112,980 lncRNAs that are expressed specifically in certain plant organs or embryos, indicating their potential role in development and organ-specific processes. In addition, CANTATAdb 3.0 includes 74,886 pairs of evolutionarily conserved lncRNAs found across 47 species and inferred from genome-genome alignments as well as conserved lncRNAs obtained using a similarity search approach in 5,479 species pairs, which would further aid in the selection of lncRNAs for functional studies. Interestingly, we find that conserved lncRNAs with tissue-specific expression patterns tend to occupy the same plant organ across different species, pointing toward conserved biological roles. The database now offers extended search capabilities and downloadable data in popular formats, further facilitating research on plant lncRNAs.

Screening of natural epigenetic modifiers for managing glycemic memory and diabetic nephropathy

Short hyperglycaemic episodes trigger metabolic memory (MM) in which managing hyperglycaemia alone is not enough to tackle the progression of Diabetic nephropathy on the epigenetic axis. We used a structural similarity search approach to identify phytochemicals similar to natural epigenetic modifiers and docked with SIRT1 protein and did ADME studies. We found that UMB was 84.3% similar to esculetin. Upon docking, we found that UMB had a binding energy of −9.2 kcal/mol while the standard ligand had −11.8 kcal/mol. ADME showed UMB to be a good lead. 2,2-diphenyl-1-picrylhydrazyl (DPPH) assay showed it to be a good antioxidant with IC50 of 107 µg/mL and MTT stands for 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyltetrazolium bromide (MTT) showed that it does not promote cell death. Oxidative biomarkers in vitro showed UMB was able to ameliorate glycemic memory induced by high glucose. Western blot revealed decreased histone acetylation under hyperglycaemic conditions and upon treatment with UMB along with DR, its levels increased. This led us to check our hypothesis of whether concomitant diet reversal (DR) together with UMB can alleviate high-fat diet-induced metabolic memory and diabetic nephropathy (DN) in SD rats. UMB was able to decrease blood glucose, lipid, renal, and liver profile concluding UMB was able to ameliorate DN and MM by increasing the histone acetylation level.

Integrating machine learning and high throughput screening for the discovery of allosteric AKT1 inhibitors

Evidence from clinical and experimental investigations reveals the role of AKT in oral cancer, which has led to the development of therapeutic and pharmacological medications for inhibiting AKT protein. Despite prodigious effort, researchers are searching for new allosteric inhibitors as orthosteric inhibitors are non-selective and exert off-target effects. In the current study, we proposed an integrated computational workflow for identifying allosteric AKT1 inhibitors as this isoform is highly correlated with poor prognosis and survival. To achieve this objective, 84 classification QSAR models with six different machine learning algorithms were developed. The models created with RDKit_RF and RDKit_kstar outperformed internal and test set validation with an ROC of 0.98. The outperformed models were then used to screen Chembl, which contains over a million compounds, for AKT1 inhibitors. The Tanimoto similarity search approach identified the compounds structurally resembling AKT allosteric inhibitors. The filtered compounds were further subjected to docking phases, molecular dynamic simulation and mmpbsa to verify the binding mode of selected ones. All these analyses suggested hit 5 (CHEMBL3948083) as the potential allosteric inhibitor of AKT1 as the stability parameters, favourable binding affinity (-107.78 ± 11.56 KJ/mol) and ligand interaction were better in comparison to other compounds and reference compound. The residual analysis demonstrated that allosteric and isoform-specific residues such as Trp80 and Val270 contributed the larger energy for ligand binding. The proposed integrated approach in this study might achieve a futuristic outcome when employed in a pharmaceutical scheme different from the conventional method. Communicated by Ramaswamy H. Sarma

D3CARP: a comprehensive platform with multiple-conformation based docking, ligand similarity search and deep learning approaches for target prediction and virtual screening

Resource- and time-consuming biological experiments are unavoidable in traditional drug discovery, which have directly driven the evolution of various computational algorithms and tools for drug-target interaction (DTI) prediction. For improving the prediction reliability, a comprehensive platform is highly expected as some previously reported webservers are small in scale, single-method, or even out of service. In this study, we integrated the multiple-conformation based docking, 2D/3D ligand similarity search and deep learning approaches to construct a comprehensive webserver, namely D3CARP, for target prediction and virtual screening. Specifically, 9352 conformations with positive control of 1970 targets were used for molecular docking, and approximately 2 million target-ligand pairs were used for 2D/3D ligand similarity search and deep learning. Besides, the positive compounds were added as references, and related diseases of therapeutic targets were annotated for further disease-based DTI study. The accuracies of the molecular docking and deep learning approaches were 0.44 and 0.89, respectively. And the average accuracy of five ligand similarity searches was 0.94. The strengths of D3CARP encompass the support for multiple computational methods, ensemble docking, utilization of positive controls as references, cross-validation of predicted outcomes, diverse disease types, and broad applicability in drug discovery. The D3CARP is freely accessible at https://www.d3pharma.com/D3CARP/index.php.

2-[(2-Amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides: Discovery of a new class of anti-tubercular agents and prospects for their further structural modification

The synthesis of 2-[(2-amino-6-methylpyrimidin-4-yl)sulfanyl]-N-arylacetamides 6a-j was encouraged by their antibacterial activity and drug-likeness predictions. Of the compounds, two bearing 4‑isopropylphenyl 6c and 2,5‑dichlorophenyl 6i moieties were found to be threefold more potent than the first-line tuberculosis drug ethambutol. A molecular docking study revealed that compound 6c may selectively bind to cyclopropane mycolic acid synthase 1, an enzyme essential for the construction of the tuberculosis bacteria cell wall. Keeping this in mind, a recently developed ligand-based virtual screening strategy combining the molecular similarity search and docking approaches was adopted to identify more potent analogs of the parent compound. As a result, a series of new ligands 18p-w with phenyl-substituted azinyl amide groups were in silico discovered. Due to their high binding affinities to the enzyme and improved toxicity profiles, the ligands are undoubtedly worth future synthetic efforts.

A Hybrid Filtering Technique of Digital Images in Multimedia Data Warehouses

The similarity search approach used for image Data Warehouse (DW) can provide better insights into discovering the most similar images compared to the input query. Due to the later innovation improvement, the mixed media complexity is discernibly expanded and modern inquires about regions are opened depending on comparable mixed media substance recovery. Content-Based Image Retrieval (CBIR) algorithms are utilized for the retrieval of images related to the inquiry image from gigantic databases or DW. The queries that are used for DW are complex, take a lot of time to process and many give less accurate results. For these reasons, this paper needs to have an effective technique to improve the similarity search query process that reflects a more positive result. In this paper, show how to extract features from a set of images (color, shape, and texture features) by using CBIR algorithm with Color Edge Detection (CED) method. Once these features are extracted, the proposed method will minimize the distance between these features vectors and the query image one using a Genetic Algorithm (GA). This paper illustrates the extraction of endless strong and imperative features from the database of the images, therefore, the capacity of these features in storing within the frame of features vectors. Accordingly, an imaginative closeness assessment with a metaheuristic algorithm (Genetic Algorithm (GA) with Simulating Annealing (SA)) has been attained between the query image features and those having a place in the database image. This paper introduces a new algorithm CEDF (Color Edge Detection with Gaussian Blur Filter) that applies the Gaussian Blur Filter after using CED method for feature detection of the image. Experimental results show that CEDF method gives better result than the other already-known methods.

An analysis of the spatial heterogeneity of future climate change impacts in support of cross-sectoral adaptation strategies in Japan

Addressing climate change requires cross-cutting and effective adaptation strategies. We presented a scenario-based, cross-scale, and spatially explicit framework for analyzing multiple future climate change impacts in Japan. The correlation analysis of climatic and multisectoral impact indicators showed that a complex set of factors, including climate and other indicators used in impact assessment models and how they were used in these models, influenced the correlations. Then, we harmonized and mapped the future impacts in Japan by coupling the K-means multivariate clustering analysis with the similarity search approach. Seven indicators of climate change impacts were utilized to identify the homogeneous impact zones (HIZs) at the end of the 21st century. The homogeneous impact maps of Japan were composed of six HIZs and two isolated impact clusters characterized by varying degrees and combinations of climate change impacts. Changing climates and the cooccurrence of various impacts were context-sensitive. The relationships between the impacts and their coefficient strengths determined the cooccurrence direction. The findings of this study aid us in understanding the compound climate risks, enhancing the synergies and cohesions of adaptation measures, and accelerating science-based adaptation strategies.

Satellite image search in AgoraEO

The growing operational capability of global Earth Observation (EO) creates new opportunities for data-driven approaches to understand and protect our planet. However, the current use of EO archives is very restricted due to the huge archive sizes and the limited exploration capabilities provided by EO platforms. To address this limitation, we have recently proposed MiLaN, a content-based image retrieval approach for fast similarity search in satellite image archives. MiLaN is a deep hashing network based on metric learning that encodes high-dimensional image features into compact binary hash codes. We use these codes as keys in a hash table to enable real-time nearest neighbor search and highly accurate retrieval. In this demonstration, we showcase the efficiency of MiLaN by integrating it with EarthQube, a browser and search engine within AgoraEO. EarthQube supports interactive visual exploration and Query-by-Example over satellite image repositories. Demo visitors will interact with EarthQube playing the role of different users that search images in a large-scale remote sensing archive by their semantic content and apply other filters.

Efficient index-free SimRank similarity search in large graphs by discounting path lengths

Link-based similarity search aims to find similar nodes for a given query node in a graph, which arises in numerous applications, including web spam detection, social network analysis and web search. Among existing methods, SimRank is a well-known similarity model, which provides an effective and trustful function for similarity search. A large amount of techniques on SimRank similarity search are devoted recently, which compute the similarity scores by traversing the paths between query and candidate nodes. However, the number of paths increases exponentially as path length increases, which makes the computation expensive and cannot support fast similarity search over large graphs. In this paper, we propose an efficient index-free SimRank similarity search approach, namely DisSim, which reduces the computational cost by discounting path length. We observe that SimRank could rapidly converge at a stable state and the results change little after a few of iterations. Based on the fast convergence, the similarity between nodes is defined as the SimRank score at the second iteration. For the computation of DisSim, we divide the similarity into one-step and two-step first-meeting probabilities. The one-step first-meeting probabilities are computed by path traverses from query to candidate nodes, which reduces computational cost by skipping unnecessary nodes. And the two-step first-meeting probabilities are computed by integrating the repeated parts of the paths. For further speeding up query processing, we develop a pruning algorithm, which prunes unpromising path traverses by setting a threshold, and the accuracy loss under threshold is given through mathematical analysis. Extensive experiments on real graphs demonstrate the performance of DisSim through comparing with the state-of-the-art algorithms.

AI based geometric similarity search supporting component reuse in engineering design

Today, companies are faced with the challenge to develop and produce individualized products in the shortest possible time at very low cost in order to remain attractive under strong competitive pressure. For reasons of efficiency, products are therefore often developed in generations. Proven components are adopted in a new product generation and only some of the components are newly developed to meet new customer requirements. Many companies, therefore, have a large database of 3D CAD product models containing years of engineering experience. Nevertheless, it is often difficult to execute database queries to find which products or components already exist and could be reused or adapted for a new product generation or variant. As a result, many duplicates are created, which are associated with high effort and costs, and the risk of introducing design errors increases.Therefore, the aim of this paper is to develop an automated approach for geometric similarity search that also takes company-specific features of components into account. Machine learning methods are capable of automatically extracting relevant geometric features by learning a suitable representation of the corresponding 3D object. For this purpose, an autoencoder is developed which is trained to extract class-specific feature vectors. To improve the representativeness of those vectors for the similarity search, the architecture and hyperparameters of the autoencoder are optimized based on several experiments. Considering a real use case with a data set from the field of mechanical engineering, it is shown that geometrically similar CAD models can be found very quickly using the learned representation, and that better results are obtained than with conventional methods based on meta information, e.g. volume and bounding box. On the one hand, the fast finding of similar models encourages the reuse of existing solutions. On the other hand, standardization and, thus, economy of scale is promoted.

LES 3

Set similarity search is a problem of central interest to a wide variety of applications such as data cleaning and web search. Past approaches on set similarity search utilize either heavy indexing structures, incurring large search costs or indexes that produce large candidate sets. In this paper, we design a learning-based exact set similarity search approach, LES 3 . Our approach first partitions sets into groups, and then utilizes a light-weight bitmap-like indexing structure, called token-group matrix (TGM), to organize groups and prune out candidates given a query set. In order to optimize pruning using the TGM, we analytically investigate the optimal partitioning strategy under certain distributional assumptions. Using these results, we then design a learning-based partitioning approach called L2P and an associated data representation encoding, PTR, to identify the partitions. We conduct extensive experiments on real and synthetic datasets to fully study LES 3 , establishing the effectiveness and superiority over other applicable approaches.

Benchmark on Indexing Algorithms for Accelerating Molecular Similarity Search.

Structurally similar analogues of given query compounds can be rapidly retrieved from chemical databases by the molecular similarity search approaches. However, the computational cost associated with the exhaustive similarity search of a large compound database will be quite high. Although the latest indexing algorithms can greatly speed up the search process, they cannot be readily applicable to molecular similarity search problems due to the lack of Tanimoto similarity metric implementation. In this paper, we first implement Python or C++ codes to enable the Tanimoto similarity search via several recent indexing algorithms, such as Hnsw and Onng. Moreover, there are increasing interests in computational communities to develop robust benchmarking systems to access the performance of various computational algorithms. Here, we provide a benchmark to evaluate the molecular similarity searching performance of these recent indexing algorithms. To avoid the potential package dependency issues, two separate benchmarks are built based on currently popular container technologies, Docker and Singularity. The Singularity container is a rather new container framework specifically designed for the high-performance computing (HPC) platform and does not need the privileged permissions or the separated daemon process. Both benchmarking methods are extensible to incorporate other new indexing algorithms, benchmarking data sets, and different customized parameter settings. Our results demonstrate that the graph-based methods, such as Hnsw and Onng, consistently achieve the best trade-off between searching effectiveness and searching efficiencies. The source code of the entire benchmark systems can be downloaded from https://github.uconn.edu/mldrugdiscovery/MssBenchmark.

Catering for unique tastes: Targeting grey-sheep users recommender systems through one-class machine learning

In recommendation systems, the grey-sheep problem refers to users with unique preferences and tastes that make it difficult to develop accurate profiles. That is, the similarity search approach typically followed during the recommendation process fails to yield good results. Most research does not focus on such users and thus fails to cater to more exotic tastes and emerging trends, leading to a subsequent loss in revenue and marketing opportunities. One suggested solution is to use one-class classification to generate a prediction list for these users, where decision boundaries are learned that distinguish between normal and grey-sheep users. In this paper, we present the grey-sheep one-class recommendation (GSOR) framework designed to create accurate prediction models while taking both regular and grey-sheep users into account. In addition, we introduce a novel grey-sheep movie recommendation benchmark to be used by current and future researchers. When evaluating our GSOR framework against this benchmark, our results indicate the value of combining cluster analysis, outlier detection, and one-class learning to generate relevant and timely recommendation lists from data sets that contain grey-sheep users. Specifically, by employing one-class decision tree algorithms, our GSOR framework was able to outperform traditional collaborative filtering-based recommendation systems in both accuracy and model construction time. Furthermore, we report that having grey-sheep users in the system often had a positive impact on the learning and recommendation processes.

EPLA-DSTree: Extending Piecewise Linear Approximation on a Dynamic Segmentation Tree Index in Sensor-Cloud Systems

In sensor-cloud applications, a huge amount of time series are generated. Efficient similarity search approaches are necessary for processing these sensor time series data. Concerning of time series search, whole matching similarity search and subsequence similarity search are two main research focuses. In this paper, we study the whole matching similarity search problem. We propose EPLA-DSTree, which extends piecewise linear approximation on a dynamic segmentation tree index for whole matching on time series. Compared with DSTree, EPLA-DSTree improves data locality of nodes by a better time series representation. EPLA-DSTree has a tighter lower bound for nodes which leads to a better query performance. Experiments show that it has a less index building time and a better query performance. To meet the requirements of sensor-cloud applications, we present an parallel EPLA-DSTree on MapReduce, which is a popular cloud programming model.

A Tree-Based Indexing Approach for Diverse Textual Similarity Search

Textual information is ubiquitous in our lives and is becoming an important component of our cognitive society. In the age of big data, we consistently need to traverse substantial amounts of data even to find a little information. To quickly acquire effective information, it is necessary to implement a textual similarity search based on an appropriate index structure to efficiently find results. In this article, we study top-k textual similarity search and develop a tree-based indexing approach that can construct indices to support various similarity functions. Our indexing approach clusters similar records in the same branch offline to improve the performance of online search. Based on the index tree, we present a top-k search algorithm with efficient pruning techniques. The experimental results demonstrate that our algorithm can achieve higher performance and better scalability than the baseline method.

A sketch-aided retrieval approach for incomplete 3D objects

With the growing amount of digital collections of visual CH data being available across different repositories, it becomes increasingly important to provide archaeologists with means to find relations and cross-correspondences between different digital records. In principle, existing shape- and image-based similarity search methods can aid such domain analysis tasks. However, in practice, visual object data are given in different modalities, and often only in incomplete or fragmented state, posing a particular challenge for conventional similarity search approaches. In this paper we introduce a methodology and system for cross-modal visual search in CH object data that addresses these challenges. Specifically, we propose a new query modality based on 3D views enhanced by user sketches (3D+sketch). This allows for adding new context to the search, which is useful e.g., for searching based on incomplete query objects, or for testing hypotheses on existence of certain shapes in a collection. We present an appropriately designed workflow for constructing query views from incomplete 3D objects enhanced by a user sketch, based on shape completion and texture inpainting. Visual cues additionally help users compare retrieved objects with the query. The proposed approach extends on a previously presented retrieval system by introducing improved retrieval methods, an extended evaluation including retrieval in a larger and richer data collection, and enhanced interactive search weight specification. We demonstrate the feasibility and potential of our approach to support analysis of domain experts in Archaeology and the field of CH in general.

Discovery of a potent small molecule inhibiting Huntington\u2019s disease\xa0(HD) pathogenesis via targeting CAG repeats RNA and Poly Q protein

CAG repeats RNA causes various fatal neurodegenerative diseases exemplified by Huntington’s disease (HD) and several spinocerebellar ataxias (SCAs). Although there are differences in the pathogenic mechanisms, these diseases share the common cause, i.e., expansion of CAG repeats. The shared cause of these diseases raises the possibility for the exploiting the common target as a potential therapeutic approach. Oligonucleotide-based therapeutics are designed earlier with the help of the base pairing rule but are not very promiscuous, considering the nonspecific stimulation of the immune system and the poor cellular delivery. Therefore, small molecules-based therapeutics are preferred for targeting the repeats expansion disorders. Here, we have used the chemical similarity search approach to discern the small molecules that selectively target toxic CAG RNA. The lead compounds showed the specificity towards AA mismatch in biophysical studies including CD, ITC, and NMR spectroscopy and thus aided to forestall the polyQ mediated pathogenicity. Furthermore, the lead compounds also explicitly alleviate the polyQ mediated toxicity in HD cell models and patient-derived cells. These findings suggest that the lead compound could act as a chemical probe for AA mismatch containing RNA as well as plays a neuroprotective role in fatal neurodegenerative diseases like HD and SCAs.

A Novel Similarity Search Approach for Streaming Time Series

Time series similarity search has been widely used in many applications, such as financial data analysis, meteorological data forecasting, and multimedia data retrieval. The original task of this research was to find time series in a database similar to the query sequence, where both the results and the query sequence are static. However, along with the arrival of Industry 4.0 era, a large number of detecting instruments in various fields have produced a large amount of time series stream data. The new challenges for Similarity-based time series retrieval can be attributed as one-pass search and real time response. In this paper, we propose a novel multi-resolution search scheme to find a set of sequences similar to the query sequence in streaming time series efficiently and exactly. The scheme performs multi-resolution filtering on the raw data, and most dissimilar sequences can be filtered out at a lower level to avoid unnecessary computation and to improve the search efficiency. Extensive experiments on different kinds of typical time series datasets have been conducted to demonstrate the superiorities of our method.

A Machine Learning Based Approach for Similarity Search on Biodiversity Knowledge Graphs

Mass biodiversity data from scientific collections will be provided by world-wide digitization efforts like iDigBio in the U.S and DiSSCo in Europe. This opens up an increasing amount of data on wild type organisms, which enables the building of large biodiversity knowledge graphs comprising, inter alia, sequence, trait and occurrence data. Knowledge graphs model information in the form of entities and their relationships expressed in good practice as ontology-based annotations. Based on ontological descriptions, semantic similarity analysis makes linking of wild type data to genomic and proteonomic data of model organisms possible and thus supports knowledge discovery of crop wild relatives and underutilized species of interest for medicine, breeding and agriculture. Since classical similarity measurements focus on recording differences between character states (aiming to describe disease phenotypes), but not the character states in the sense of trait variations itself, new methods for similarity search are required. Machine learning algorithms operate on feature vectors, which are numeric representations of data (images, class labels etc) in n-dimensional vector space. We established a machine learning based workflow for similarity search on biodiversity entities using feature learning on ontologies and an associated RDF knowledge graph to project structured trait data into vector space. Vectors are then compared applying a similarity function (e.g. cosine similarity) to determine similarity between taxa based on trait semantics. We will present an application example of machine learning on biodiversity knowledge graphs using a pipeline built upon OPA2Vec, a method to generate feature vectors from the logical content of ontologies (Smaili et al. 2018), to successfully cluster plant species for life form and ecotype (e.g. tree vs. perennial plant) on the basis of their annotations with the Flora Phenotype Ontology (Hoehndorf et al. 2016).