Dataset Information

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

ABSTRACT: A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership).We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows exploration with a variety of interfaces. For efficiency, we also provide relational-database access and translation between the relational and RDF versions. LinkHub is practically useful in creating small, local hubs on common topics and then connecting these to major portals in a federated architecture; we have used LinkHub to establish such a relationship between UniProt and the North East Structural Genomics Consortium. LinkHub also facilitates queries and access to information and documents related to identifiers spread across multiple databases, acting as "connecting glue" between different identifier spaces. We demonstrate this with example queries discovering "interologs" of yeast protein interactions in the worm and exploring the relationship between gene essentiality and pseudogene content. We also show how "protein family based" retrieval of documents can be achieved. LinkHub is available at hub.gersteinlab.org and hub.nesg.org with supplement, database models and full-source code.LinkHub leverages Semantic Web standards-based integrated data to provide novel information retrieval to identifier-related documents through relational graph queries, simplifies and manages connections to major hubs such as UniProt, and provides useful interactive and query interfaces for exploring the integrated data.

SUBMITTER: Smith AK

PROVIDER: S-EPMC1892102 | biostudies-literature | 2007 May

REPOSITORIES: biostudies-literature

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LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

Smith Andrew K AK Cheung Kei-Hoi KH Yip Kevin Y KY Schultz Martin M Gerstein Mark K MK

BMC bioinformatics 20070509

<h4>Background</h4>A key abstraction in representing proteomics knowledge is the notion of unique identifiers for individual entities (e.g. proteins) and the massive graph of relationships among them. These relationships are sometimes simple (e.g. synonyms) but are often more complex (e.g. one-to-many relationships in protein family membership).<h4>Results</h4>We have built a software system called LinkHub using Semantic Web RDF that manages the graph of identifier relationships and allows explo ...[more]

PMID: 17493288

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Project description:BACKGROUND: The amount of information stemming from proteomics experiments involving (multi dimensional) separation techniques, mass spectrometric analysis, and computational analysis is ever-increasing. Data from such an experimental workflow needs to be captured, related and analyzed. Biological experiments within this scope produce heterogenic data ranging from pictures of one or two-dimensional protein maps and spectra recorded by tandem mass spectrometry to text-based identifications made by algorithms which analyze these spectra. Additionally, peptide and corresponding protein information needs to be displayed. RESULTS: In order to handle the large amount of data from computational processing of mass spectrometric experiments, automatic import scripts are available and the necessity for manual input to the database has been minimized. Information is in a generic format which abstracts from specific software tools typically used in such an experimental workflow. The software is therefore capable of storing and cross analysing results from many algorithms. A novel feature and a focus of this database is to facilitate protein identification by using peptides identified from mass spectrometry and link this information directly to respective protein maps. Additionally, our application employs spectral counting for quantitative presentation of the data. All information can be linked to hot spots on images to place the results into an experimental context. A summary of identified proteins, containing all relevant information per hot spot, is automatically generated, usually upon either a change in the underlying protein models or due to newly imported identifications. The supporting information for this report can be accessed in multiple ways using the user interface provided by the application. CONCLUSION: We present a proteomics database which aims to greatly reduce evaluation time of results from mass spectrometric experiments and enhance result quality by allowing consistent data handling. Import functionality, automatic protein detection, and summary creation act together to facilitate data analysis. In addition, supporting information for these findings is readily accessible via the graphical user interface provided. The database schema and the implementation, which can easily be installed on virtually any server, can be downloaded in the form of a compressed file from our project webpage.

Project description:BACKGROUND:The right dataset is essential to obtain the right insights in data science; therefore, it is important for data scientists to have a good understanding of the availability of relevant datasets as well as the content, structure, and existing analyses of these datasets. While a number of efforts are underway to integrate the large amount and variety of datasets, the lack of an information resource that focuses on specific needs of target users of datasets has existed as a problem for years. To address this gap, we have developed a Dataset Information Resource (DIR), using a user-oriented approach, which gathers relevant dataset knowledge for specific user types. In the present version, we specifically address the challenges of entry-level data scientists in learning to identify, understand, and analyze major datasets in healthcare. We emphasize that the DIR does not contain actual data from the datasets but aims to provide comprehensive knowledge about the datasets and their analyses. METHODS:The DIR leverages Semantic Web technologies and the W3C Dataset Description Profile as the standard for knowledge integration and representation. To extract tailored knowledge for target users, we have developed methods for manual extractions from dataset documentations as well as semi-automatic extractions from related publications, using natural language processing (NLP)-based approaches. A semantic query component is available for knowledge retrieval, and a parameterized question-answering functionality is provided to facilitate the ease of search. RESULTS:The DIR prototype is composed of four major components-dataset metadata and related knowledge, search modules, question answering for frequently-asked questions, and blogs. The current implementation includes information on 12 commonly used large and complex healthcare datasets. The initial usage evaluation based on health informatics novices indicates that the DIR is helpful and beginner-friendly. CONCLUSIONS:We have developed a novel user-oriented DIR that provides dataset knowledge specialized for target user groups. Knowledge about datasets is effectively represented in the Semantic Web. At this initial stage, the DIR has already been able to provide sophisticated and relevant knowledge of 12 datasets to help entry health informacians learn healthcare data analysis using suitable datasets. Further development of both content and function levels is underway.

Project description:Purpose Recent behavioral studies have demonstrated the effectiveness of implementing retrieval practice into learning tasks for children. Such approaches have revealed that repeated spaced retrieval (RSR) is particularly effective in promoting children's learning of word form and meaning information. This study further examines how retrieval practice enhances learning of word meaning information at the behavioral and neural levels. Method Twenty typically developing preschool children were taught novel words using an RSR learning schedule for some words and an immediate retrieval (IR) learning schedule for other words. In addition to the label, children were taught two arbitrary semantic features for each item. Following the teaching phase, children's learning was tested using recall tests. In addition, during the 1-week follow-up, children were presented with pictures and an auditory sentence that correctly labeled the item but stated correct or incorrect semantic information. Event-related brain potentials (ERPs) were time locked to the onset of the words noting the semantic feature. Children provided verbal judgments of whether the semantic feature was correctly paired with the item. Results Children recalled more labels and semantic features for items that had been taught in the RSR learning schedule relative to the IR learning schedule. ERPs also differentiated the learning schedules. Mismatching label-meaning pairings elicited an N400 and late positive component (LPC) for both learning conditions; however, mismatching RSR pairs elicited an N400 with an earlier onset and an LPC with a longer duration, relative to IR mismatching label-meaning pairings. These ERP timing differences indicated that the children were more efficient in processing words that were taught in the RSR schedule relative to the IR learning schedule. Conclusions Spaced retrieval practice promotes learning of both word form and meaning information. The findings lay the necessary groundwork for better understanding of processing newly learned semantic information in preschool children. Supplemental Material https://doi.org/10.23641/asha.15063060.

Dataset Information

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

Publications

LinkHub: a Semantic Web system that facilitates cross-database queries and information retrieval in proteomics.

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