Formulation Of Complex Queries Research Articles

Exploring Chemical Information in PubChem.

PubChem (https://pubchem.ncbi.nlm.nih.gov) is a public chemical database that serves scientific communities as well as the general public. This database collects chemical information from hundreds of data sources and organizes them into multiple data collections, including Substance, Compound, BioAssay, Protein, Gene, Pathway, and Patent. These collections are interlinked with each other, allowing users to discover related records in the various collections (e.g., drugs targeting a protein or genes modulated by a chemical). PubChem can be searched by keyword (e.g., a chemical, protein, or gene name) as well as by chemical structure. The input structure can be provided using popular line notations or drawn with the PubChem Sketcher. PubChem supports various types of structure searches, including identity search, 2‐D and 3‐D similarity searches, and substructure and superstructure searches. Results from multiple searches can be combined using Boolean operators (i.e., AND, OR, and NOT) to formulate complex queries. PubChem allows the user to quickly retrieve a list of records annotated with a particular classification or ontological term. This paper provides step‐by‐step instructions on how to explore PubChem data with examples of commonly requested tasks. © 2021. This article is a U.S. Government work and is in the public domain in the USA. Current Protocols published by Wiley Periodicals LLC.Basic Protocol 1: Finding genes and proteins that interact with a given compoundBasic Protocol 2: Finding drug‐like compounds similar to a query compound through a two‐dimensional (2‐D) similarity searchBasic Protocol 3: Finding compounds similar to a query compound through a three‐dimensional (3‐D) similarity searchSupport Protocol: Computing similarity scores between compoundsBasic Protocol 4: Getting the bioactivity data for the hit compounds from substructure searchBasic Protocol 5: Finding drugs that target a particular geneBasic Protocol 6: Getting bioactivity data of all chemicals tested against a protein.Basic Protocol 7: Finding compounds annotated with classifications or ontological termsBasic Protocol 8: Finding stereoisomers and isotopomers of a compound through identity search

The HLA Ligand Atlas: A novel immuno-oncology resource for T-cell antigen discovery.

3128 Background: The human leukocyte antigen (HLA) complex regulates the adaptive immune response by showcasing the intracellular and extracellular protein content to the immune system, which is the basis for T cell-dependent tumor rejection. Therefore, a comprehensive map of the entirety of both HLA class I- and class II-presented peptides from various benign tissues is a highly sought after resource, as it enables the definition of tumor-association on the immunologically pivotal level of the HLA ligandome. Methods: Human tissue samples were snap frozen post mortem during autopsy. The study was approved by the local IRB. HLA ligands were immunopurified and characterized using an Orbitrap Fusion Lumos mass spectrometer coupled to an Ultimate 3000 RSLC Nano UHPLC System. Data acquisition was performed as technical triplicates in data-dependent mode, and data were analyzed using the containerized, computational pipeline MHCquant. Results: In this work, we describe the HLA Ligand Atlas, a comprehensive collection of matched HLA class I and class II ligandomes from 29 non-malignant tissues and 13 human subjects (208 samples in total), covering 38 HLA class I, and 17 HLA*DRB alleles and comprising 48,381 HLA class I and 16,146 HLA class II peptides. Nearly 50% of HLA ligands have not been previously described. The HLA Ligand Atlas is publicly available as a raw data resource, but also in the form of a user-friendly web interface that allows users to quickly formulate complex queries against the data set. Both downloadable data and the query interface are available at www.hla-ligand-atlas.org. Conclusions: This data set provides a valuable tool for research in diverse fields such as systems biology, general immunology, autoimmune disease and organ transplantation. Most importantly, the HLA Ligand Atlas provides essential information for translational applications in immuno-oncology. The knowledge of HLA ligands from benign tissues strongly supports the informed design of proteogenomic HLA-dependent target discovery approaches.

GraphTQL: A visual query system for graph databases

Abstract End-users who are experts in an application domain need to retrieve data from databases in order to answer particular inquiries. Visual Query Systems (VQSs) facilitate this task; but, formulating complex queries is still challenging for end-users. In particular, VQSs for graph databases can be broadly classified into two groups: One group is based on data exploration and allow for direct manipulation of data graphs, but with limited query expressiveness. The other group, oriented to build visual sentences that represent the query, provide greater expressiveness, but they transfer complex language concepts and operations to the visual language. Thus, this research developed GraphTQL a visual query system that provides greater expressiveness and ease in the query formulation. GraphTQL uses a graph data model to depict the domain of interest at the conceptual level. A set of operators provides the means to transform schema and instance graphs, reducing them progressively to obtain the data of interest. The operators include implicit management of incomplete data; as well as clarification dialogues to guide users in order to express complex filter conditions and to select the paths which connect entities in the graph. The development of GraphTQL followed a user-centered design approach which focused on the medical domain. Thus, potential users’ contribution and feedback played a significant role in this work. A comparison with a graphical query interface for SPARQL showed better results for GraphTQL regarding the number of queries correctly formulated by users, the number of errors and the time spent for query formulation.

MapInteract 2014 Workshop Report the 2nd ACM SIGSPATIAL International Workshop on Interacting with Maps Dallas, Texas, USA - November 4, 2014

Maps are rapidly becoming fully interactive interfaces to geographic information. We use them as tools to plan our journeys, to decide where we are going to live, to visualize data, or for gaming. We want to inspect relationships between entities, navigate through large result sets, and to quickly identify the best options amongst many alternatives. Whatever it is, we need to express our needs such that algorithms can provide the answer. However, working with maps is still surprisingly awkward. Simple queries can require a lot of interaction and workarounds, formulating complex queries is sometimes not even possible. To transform maps into intelligent and interactive interfaces, we need to anticipate users, contexts, and tasks and adapt interaction and visualization towards their needs and capabilities. MapInteract provides a platform to discuss these challenges and to shape the picture of maps of the future.

A Framework for Automatic Video Surveillance Indexing and Retrieval

The manual search through the surveillance video archives for a specific object or event is very time-consuming and tedious task due to the large volume of video data captured by many installed surveillance cameras. Therefore, the solution to accelerate and facilitate this process is to design an automatic video surveillance with the efficient and effective video indexing, video data model, query formulation and language, as well as visualization interface. There are many challenges, for developing a powerful query processing module, formulating complex queries and selecting suitable similarity matching strategy to detect any abnormality based on semantic content of the video using various query types. This study presents a novel video surveillance indexing and retrieval framework to cope with the above challenges. The proposed framework consists of three main modules i.e., pre-processing, query processing and retrieval processing. Moreover, it supports an efficient search and actively refines the retrieval result by formulating various query types including: query-by-text, query-by-example and query-by-region.

A study on parallelizing XML path filtering using accelerators

Publish-subscribe systems present the state of the art in information dissemination to multiple users. Such systems have evolved from simple topic-based to the current XML-based systems. XML-based pub-sub systems provide users with more flexibility by allowing the formulation of complex queries on the content as well as the structure of the streaming messages. Messages that match a given user query are forwarded to the user. This article examines how to exploit the parallelism found in XPath filtering. Using an incoming XML stream, parsing and matching thousands of user profiles are performed simultaneously by matching engines. We show the benefits and trade-offs of mapping the proposed filtering approach onto FPGAs, processing streams of XML at wire speed, and GPUs, providing the flexibility of software. This is in contrast to conventional approaches bound by the sequential aspect of software computing, associated with a large memory footprint. By converting XPath expressions into custom stacks, our solution is the first to provide support for complex XPath structural constructs, such as parent-child and ancestor descendant relations, whilst allowing wildcarding and recursion. The measured speedups resulting from the GPU and FPGA accelerations versus single-core CPUs are up to 6.6X and 2.5 orders of magnitude, respectively. The FPGA approaches are up to 31X faster than software running on 12 CPU cores.

Design and implementation of Metta, a metasearch engine for biomedical literature retrieval intended for systematic reviewers.

BackgroundIndividuals and groups who write systematic reviews and meta-analyses in evidence-based medicine regularly carry out literature searches across multiple search engines linked to different bibliographic databases, and thus have an urgent need for a suitable metasearch engine to save time spent on repeated searches and to remove duplicate publications from initial consideration. Unlike general users who generally carry out searches to find a few highly relevant (or highly recent) articles, systematic reviewers seek to obtain a comprehensive set of articles on a given topic, satisfying specific criteria. This creates special requirements and challenges for metasearch engine design and implementation.MethodsWe created a federated search tool that is connected to five databases: PubMed, EMBASE, CINAHL, PsycINFO, and the Cochrane Central Register of Controlled Trials. Retrieved bibliographic records were shown online; optionally, results could be de-duplicated and exported in both BibTex and XML format.ResultsThe query interface was extensively modified in response to feedback from users within our team. Besides a general search track and one focused on human-related articles, we also added search tracks optimized to identify case reports and systematic reviews. Although users could modify preset search options, they were rarely if ever altered in practice. Up to several thousand retrieved records could be exported within a few minutes. De-duplication of records returned from multiple databases was carried out in a prioritized fashion that favored retaining citations returned from PubMed.ConclusionsSystematic reviewers are used to formulating complex queries using strategies and search tags that are specific for individual databases. Metta offers a different approach that may save substantial time but which requires modification of current search strategies and better indexing of randomized controlled trial articles. We envision Metta as one piece of a multi-tool pipeline that will assist systematic reviewers in retrieving, filtering and assessing publications. As such, Metta may find wide utility for anyone who is carrying out a comprehensive search of the biomedical literature.Electronic supplementary materialThe online version of this article (doi:10.1186/2047-2501-2-1) contains supplementary material, which is available to authorized users.

PROLIX: Rapid Mining of Protein–Ligand Interactions in Large Crystal Structure Databases

A central problem in structure-based drug design is understanding protein-ligand interactions quantitatively and qualitatively. Several recent studies have highlighted from a qualitative perspective the nature of these interactions and their utility in drug discovery. However, a common limitation is a lack of adequate tools to mine these interactions comprehensively, since exhaustive searches of the protein data bank are time-consuming and difficult to perform. Consequently, fundamental questions remain unanswered: How unique or how common are the protein-ligand interactions observed in a given drug design project when compared to all complexed structures in the protein data bank? Which interaction patterns might explain the affinity of a tool compound toward unwanted targets? To answer these questions and to enable the systematic and comprehensive study of protein-ligand interactions, we introduce PROLIX (Protein Ligand Interaction Explorer), a tool that uses sophisticated fingerprint representations of protein-ligand interaction patterns for rapid data mining in large crystal structure databases. Our implementation strategy pursues a branch-and-bound technique that enables mining against thousands of complexes within a few seconds. Key elements of PROLIX include (i) an intuitive interface that enables users to formulate complex queries easily, (ii) exceptional speed for results retrieval, and (iii) a sophisticated results summarization. Herein we describe the algorithms developed to enable complex queries and fast retrieval of search results, as well as the intuitive aspects of the user interface and summarization viewer.

FromDaDy: Spreading Aircraft Trajectories Across Views to Support Iterative Queries

When displaying thousands of aircraft trajectories on a screen, the visualization is spoiled by a tangle of trails. The visual analysis is therefore difficult, especially if a specific class of trajectories in an erroneous dataset has to be studied. We designed FromDaDy, a trajectory visualization tool that tackles the difficulties of exploring the visualization of multiple trails. This multidimensional data exploration is based on scatterplots, brushing, pick and drop, juxtaposed views and rapid visual design. Users can organize the workspace composed of multiple juxtaposed views. They can define the visual configuration of the views by connecting data dimensions from the dataset to Bertin's visual variables. They can then brush trajectories, and with a pick and drop operation they can spread the brushed information across views. They can then repeat these interactions, until they extract a set of relevant data, thus formulating complex queries. Through two real-world scenarios, we show how FromDaDy supports iterative queries and the extraction of trajectories in a dataset that contains up to 5 million data.

A hierarchical approach to model web query interfaces for web source integration

Much data in the Web is hidden behind Web query interfaces. In most cases the only means to "surface" the content of a Web database is by formulating complex queries on such interfaces. Applications such as Deep Web crawling and Web database integration require an automatic usage of these interfaces. Therefore, an important problem to be addressed is the automatic extraction of query interfaces into an appropriate model. We hypothesize the existence of a set of domain-independent "commonsense design rules" that guides the creation of Web query interfaces. These rules transform query interfaces into schema trees. In this paper we describe a Web query interface extraction algorithm, which combines HTML tokens and the geometric layout of these tokens within a Web page. Tokens are classified into several classes out of which the most significant ones are text tokens and field tokens. A tree structure is derived for text tokens using their geometric layout. Another tree structure is derived for the field tokens. The hierarchical representation of a query interface is obtained by iteratively merging these two trees. Thus, we convert the extraction problem into an integration problem. Our experiments show the promise of our algorithm: it outperforms the previous approaches on extracting query interfaces on about 6.5% in accuracy as evaluated over three corpora with more than 500 Deep Web interfaces from 15 different domains.

A virtual file system for the PubChem chemical structure and bioassay database

The PubChem chemical structure and bioassay database (http://pubchem.ncbi.nlm.nih.gov) has established itself as one of the premier information sources for chemical structures and assay data accessible via the Internet. PubChem provides a convenient interactive Web interface for the execution and result display of standard structure and text-based queries. However, the capabilities for formulating complex queries, and to access and download specific data sets, are limited. Because of the necessity to integrate PubChem into the existing Entrez framework, instead of designing a new streamlined interface for handling its peculiar data content, in many cases queries are awkward to set up and execute if they become more complex. Recently, PubChem has published a specification for its Power User Gateway (PUG). This interface allows enterprising users to retrieve selected PubChem data via an XML-based Web service. The text-oriented cluster of Entrez databases has been accessible for a long time via similarly structured access modules termed Eutils. Eutils also support access to the text components of the PubChem deposition structure and standardized compound databases. Finally, individual record downloads and similar operations are possible via creatively crafted Web URLs, mimicking those encountered in interactive sessions. By virtue of these mechanisms, PubChem does in principle provide a larger degree of programmatic accessibility than other chemistry Web databases. Nevertheless, they are extremely difficult to use in their native, raw form. In order to address this difficulty, we have implemented a virtual file I/O module for the Cactvs Chemoinformatics Toolkit. It provides access to the PubChem compound database as a virtual file. The supported feature set starts with simple record-based I/O and extends to the execution of structure queries of higher complexity than possible via the PUG. Users of the toolkit may now script the same toolkit commands for the PubChem database as they can for a local read-only structure file. Behind the scenes, the I/O module leverages the described three access mechanisms in an optimized fashion, re-routing as many of the operations needed to perform the command to the PubChem servers as possible, but also transparently utilizing downloaded records for local processing in case operations are requested which exceed the capabilities of the systems operating on the PubChem site. A strength of our implementation is that it fully understands the native ASN.1 data specification for the database contents and is therefore not restricted to working on the sometimes coarse approximations of structure configuration which are available as SD-file records. With this work, we think we are presenting the first usable solution for scripted access to the PubChem database. Given its emerging importance for drug-related research, we hope that this software will be generally useful for a broad audience.

PAT: a protein analysis toolkit for integrated biocomputing on the web.

PAT, for Protein Analysis Toolkit, is an integrated biocomputing server. The main goal of its design was to facilitate the combination of different processing tools for complex protein analyses and to simplify the automation of repetitive tasks. The PAT server provides a standardized web interface to a wide range of protein analysis tools. It is designed as a streamlined analysis environment that implements many features which strongly simplify studies dealing with protein sequences and structures and improve productivity. PAT is able to read and write data in many bioinformatics formats and to create any desired pipeline by seamlessly sending the output of a tool to the input of another tool. PAT can retrieve protein entries from identifier-based queries by using pre-computed database indexes. Users can easily formulate complex queries combining different analysis tools with few mouse clicks, or via a dedicated macro language, and a web session manager provides direct access to any temporary file generated during the user session. PAT is freely accessible on the Internet at .

WHY YOU SHOULD USE A RELATIONAL DATABASE INSTEAD OF A SPREADSHEET

Spreadsheets have become powerful tools and are used widely nowadays. Relational databases seem to disappear into information systems either just for presenting data or forming the basis of complex systems integrating, for example, business rules. It will be shown that—following their rules—relational databases are able to support research in a very flexible manner. The structure of the underlying data may be very simple (just one table) or more complex (consisting of several related tables often according to normalization rules). In each case, the nonprocedural query to the database can give insights from different perspectives assuming that the underlying data are consistent and the often nontrivial query is formulated correctly. Examples will be given in the context of the evaluation of material flows, demonstrating the importance of data consistency, the advantages of the relational data model (and its limitations), and the power that is given by a modern, flexible interface which frees the scientist from explicitly formulating complex queries which are then prone to error. Finally it will be discussed why the spreadsheet is nevertheless used so often when a database is more appropriate.

Web-accessible proteome databases for microbial research.

The analysis of proteomes of biological organisms represents a major challenge of the post-genome era. Classical proteomics combines two-dimensional electrophoresis (2-DE) and mass spectrometry (MS) for the identification of proteins. Novel technologies such as isotope coded affinity tag (ICAT)-liquid chromatography/mass spectrometry (LC/MS) open new insights into protein alterations. The vast amount and diverse types of proteomic data require adequate web-accessible computational and database technologies for storage, integration, dissemination, analysis and visualization. A proteome database system (http://www.mpiib-berlin.mpg.de/2D-PAGE) for microbial research has been constructed which integrates 2-DE/MS, ICAT-LC/MS and functional classification data of proteins with genomic, metabolic and other biological knowledge sources. The two-dimensional polyacrylamide gel electrophoresis database delivers experimental data on microbial proteins including mass spectra for the validation of protein identification. The ICAT-LC/MS database comprises experimental data for protein alterations of mycobacterial strains BCG vs. H37Rv. By formulating complex queries within a functional protein classification database "FUNC_CLASS" for Mycobacterium tuberculosis and Helicobacter pylori the researcher can gather precise information on genes, proteins, protein classes and metabolic pathways. The use of the R language in the database architecture allows high-level data analysis and visualization to be performed "on-the-fly". The database system is centrally administrated, and investigators without specific bioinformatic competence in database construction can submit their data. The database system also serves as a template for a prototype of a European Proteome Database of Pathogenic Bacteria. Currently, the database system includes proteome information for six strains of microorganisms.

ABI: analogy-based indexing for content image retrieval

Morphogeometric-based metrics are not always appropriate to describe high-level contents of images as well as to formulate complex queries. People often find that two pictures as similar because they share relational predicates rather than objects attributes. In particular, images can be related because they are analogous. Scientists, for example, use analogies to trace art influences across different paints. In this paper, we focus on analogous relationships between groups of objects. The model we propose combines primitive properties by mean of a logical reasoning engine to produce a hierarchical image description. Each picture is decomposed into its spatial relations (physical layer), cognitive relations between objects within a group (group layer), and relations between groups (meta-group layer). This new Analogy Based Indexing (ABI for short) for Content Image Retrieval, allows users to express complex queries such as search for functional associations or group membership relations. A proof-of-concept prototype is also discussed to verify the precision and the efficiency of the proposed system. Furthermore, an embedded visual language enables pictorial queries composition and simplifies image annotation. The experimental results show the effectiveness of ABI in terms of precision vs. recall curve diagrams.

Converting Paradoxs QBE Set Queries Into Access 2000 SQL

One of the most important promises of the move to an SQL-based accounting software package has been that it frees the accountant from the necessity of resorting to a programmer when retrieving information from the organization's database in response to unanticipated managerial needs. That promise is founded, in part, on the availability of a very high-level, visual relational query language interface known as Query By Example (QBE). Unfortunately, the implementation of QBE in Microsoft Access 2000 fails to support users in formulating complex queries involving set comparison that tend to arise in on-line analytical processing (OLAP) situations. And, while Paradoxs implementation of QBE makes the formulation of such queries quite intuitive, its built-in SQL translation feature fails to provide a clue on how to convert such queries into SQL. This paper presents a systematic approach based on formulating complex set queries in Paradoxs richer QBE notation and translating them into SQL queries that can be handled by Access 2000.

Procedural and non-procedural query languages revisited: a comparison of relational algebra and relational calculus

The performance of a single group of subjects on four queries of varying degrees of difficulty, written in both relational algebra and in relational calculus, is measured. The experiment indicates a definite superiority of algebra over calculus in the formulation of complex queries. Varying approaches to query formulation in the two languages are discussed and analysed. Differences between the complexity levels of the languages are illustrated via three forms of a single query.

The Role of Artificial Intelligence in the Integration of Remotely Sensed Data with Geographic Information Systems

Although databases for geographic information systems (GIS) have been developed to manage digital map data, the integration of remotely sensed imagery and other collateral non-map information is rarely performed. For the most part, the use of sophisticated intelligent spatial databases, in which the user can query interactively about map, terrain, or associated imagery, is unknown in the GIS and cartographic community. In standard GIS systems, the ability to formulate complex queries requiring dynamic computation of factual and geometric properties is severely limited, often reflecting its origin as collections of thematic map overlays. Spatial database research requires the integration of ideas and techniques from many disciplines such as computer graphics, computational geometry, database methodology, image analysis, photogrammetry, and artificial intelligence. In this paper we discuss some ideas on how the scope of geographic information systems can be expanded by utilizing techniques from the Al community that may remedy deficiencies in user interfaces, spatial data representation, and its utilization. We draw on ongoing research at Carnegie Mellon University for examples of these techniques in the areas of image/map database and knowledge-based image interpretation.