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BioBIKE: a Web-based, programmable, integrated biological knowledge base.

ABSTRACT: BioBIKE (biobike.csbc.vcu.edu) is a web-based environment enabling biologists with little programming expertise to combine tools, data, and knowledge in novel and possibly complex ways, as demanded by the biological problem at hand. BioBIKE is composed of three integrated components: a biological knowledge base, a graphical programming interface and an extensible set of tools. Each of the five current BioBIKE instances provides all available information (genomic, metabolic, experimental) appropriate to a given research community. The BioBIKE programming language and graphical programming interface employ familiar operations to help users combine functions and information to conduct biologically meaningful analyses. Many commonly used tools, such as Blast and PHYLIP, are built-in, allowing users to access them within the same interface and to pass results from one to another. Users may also invent their own tools, packaging complex expressions under a single name, which is immediately made accessible through the graphical interface. BioBIKE represents a partial solution to the difficult question of how to enable those with no background in computer programming to work directly and creatively with mass biological information. BioBIKE is distributed under the MIT Open Source license. A description of the underlying language and other technical matters is available at www.Biobike.org.

SUBMITTER: Elhai J

PROVIDER: S-EPMC2703918 | biostudies-literature | 2009 Jul

REPOSITORIES: biostudies-literature

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BioBIKE: a Web-based, programmable, integrated biological knowledge base.

Elhai Jeff J Taton Arnaud A Massar J P JP Myers John K JK Travers Mike M Casey Johnny J Slupesky Mark M Shrager Jeff J

Nucleic acids research 20090511 Web Server issue

BioBIKE (biobike.csbc.vcu.edu) is a web-based environment enabling biologists with little programming expertise to combine tools, data, and knowledge in novel and possibly complex ways, as demanded by the biological problem at hand. BioBIKE is composed of three integrated components: a biological knowledge base, a graphical programming interface and an extensible set of tools. Each of the five current BioBIKE instances provides all available information (genomic, metabolic, experimental) appropr ...[more]

PMID: 19433511

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Project description:BackgroundOne in five U.S. adults lives with some kind of mental health condition and 4.6% of all U.S. adults have a serious mental illness. The Internet has become the first place for these people to seek online mental health information for help. However, online mental health information is not well-organized and often of low quality. There have been efforts in building evidence-based mental health knowledgebases curated with information manually extracted from the high-quality scientific literature. Manual extraction is inefficient. Crowdsourcing can potentially be a low-cost mechanism to collect labeled data from non-expert laypeople. However, there is not an existing annotation tool integrated with popular crowdsourcing platforms to perform the information extraction tasks. In our previous work, we prototyped a Semantic Text Annotation Tool (STAT) to address this gap.ObjectiveWe aimed to refine the STAT prototype (1) to improve its usability and (2) to enhance the crowdsourcing workflow efficiency to facilitate the construction of evidence-based mental health knowledgebase, following a user-centered design (UCD) approach.MethodsFollowing UCD principles, we conducted four design iterations to improve the initial STAT prototype. In the first two iterations, usability testing focus groups were conducted internally with 8 participants recruited from a convenient sample, and the usability was evaluated with a modified System Usability Scale (SUS). In the following two iterations, usability testing was conducted externally using the Amazon Mechanical Turk (MTurk) platform. In each iteration, we summarized the usability testing results through thematic analysis, identified usability issues, and conducted a heuristic evaluation to map identified usability issues to Jakob Nielsen's usability heuristics. We collected suggested improvements in the usability testing sessions and enhanced STAT accordingly in the next UCD iteration. After four UCD iterations, we conducted a case study of the system on MTurk using mental health related scientific literature. We compared the performance of crowdsourcing workers with two expert annotators from two aspects: efficiency and quality.ResultsThe SUS score increased from 70.3 ± 12.5 to 81.1 ± 9.8 after the two internal UCD iterations as we improved STAT's functionality based on the suggested improvements. We then evaluated STAT externally through MTurk in the following two iterations. The SUS score decreased to 55.7 ± 20.1 in the third iteration, probably because of the complexity of the tasks. After further simplification of STAT and the annotation tasks with an improved annotation guideline, the SUS score increased to 73.8 ± 13.8 in the fourth iteration of UCD. In the evaluation case study, on average, the workers spent 125.5 ± 69.2 s on the onboarding tutorial and the crowdsourcing workers spent significantly less time on the annotation tasks compared to the two experts. In terms of annotation quality, the workers' annotation results achieved average F1-scores ranged from 0.62 to 0.84 for the different sentences.ConclusionsWe successfully developed a web-based semantic text annotation tool, STAT, to facilitate the curation of semantic web knowledgebases through four UCD iterations. The lessons learned from the UCD process could serve as a guide to further enhance STAT and the development and design of other crowdsourcing-based semantic text annotation tasks. Our study also showed that a well-organized, informative annotation guideline is as important as the annotation tool itself. Further, we learned that a crowdsourcing task should consist of multiple simple microtasks rather than a complicated task.

Project description:BackgroundIntegrated knowledge translation (IKT) is a model of research co-production, whereby researchers partner with knowledge users throughout the research process and who can use the research recommendations in practice or policy. IKT approaches are used to improve the relevance and impact of research. As an emerging field, however, the evidence underpinning IKT is in active development. The Integrated Knowledge Translation Research Network represents a collaborative interdisciplinary team that aims to advance the state of IKT science.MethodsIn 2017, the Integrated Knowledge Translation Research Network issued a call to its members for concept papers to further define IKT, outline an IKT research agenda, and inform the Integrated Knowledge Translation Research Network's special meeting entitled, Integrated Knowledge Translation State of the Science Colloquium, in Ottawa, Canada (2018). At the colloquium, authors presented concept papers and discussed knowledge-gaps for a research agenda and implications for advancing the IKT field. We took detailed field notes, audio-recorded the meeting and analysed the data using qualitative content analysis.ResultsTwenty-four participants attended the meeting, including researchers (n = 11), trainees (n = 6) and knowledge users (n = 7). Seven overarching categories emerged from these proceedings - IKT theory, IKT methods, IKT process, promoting partnership, definitions and distinctions of key IKT terms, capacity-building, and role of funders. Within these categories, priorities identified for future IKT research included: (1) improving clarity about research co-production/IKT theories and frameworks; (2) describing the process for engaging knowledge users; and (3) identifying research co-production/IKT outcomes and methods for evaluation.ConclusionThe Integrated Knowledge Translation State of the Science Colloquium initiated a research agenda to advance IKT science and practice. Next steps will focus on building a theoretical and evidence base for IKT.

Dataset Information

BioBIKE: a Web-based, programmable, integrated biological knowledge base.

Publications

BioBIKE: a Web-based, programmable, integrated biological knowledge base.

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