Dataset Information

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

ABSTRACT: Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 dictionaries and combination of these terms. To illustrate the utility of DES-TOMATO, we provide several examples how one can efficiently use this KB to retrieve known or potentially novel information. DES-TOMATO is free for academic and nonprofit users and can be accessed at http://cbrc.kaust.edu.sa/des_tomato/, using any of the mainstream web browsers, including Firefox, Safari and Chrome.

SUBMITTER: Salhi A

PROVIDER: S-EPMC5519719 | biostudies-literature | 2017 Jul

REPOSITORIES: biostudies-literature

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Publications

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

Salhi Adil A Negrão Sónia S Essack Magbubah M Morton Mitchell J L MJL Bougouffa Salim S Razali Rozaimi R Radovanovic Aleksandar A Marchand Benoit B Kulmanov Maxat M Hoehndorf Robert R Tester Mark M Bajic Vladimir B VB

Scientific reports 20170720 1

Tomato is the most economically important horticultural crop used as a model to study plant biology and particularly fruit development. Knowledge obtained from tomato research initiated improvements in tomato and, being transferrable to other such economically important crops, has led to a surge of tomato-related research and published literature. We developed DES-TOMATO knowledgebase (KB) for exploration of information related to tomato. Information exploration is enabled through terms from 26 ...[more]

PMID: 28729549

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Project description:BackgroundBiomedical knowledge grows in complexity, and becomes encoded in network-based repositories, which include focused, expert-drawn diagrams, networks of evidence-based associations and established ontologies. Combining these structured information sources is an important computational challenge, as large graphs are difficult to analyze visually.ResultsWe investigate knowledge discovery in manually curated and annotated molecular interaction diagrams. To evaluate similarity of content we use: i) Euclidean distance in expert-drawn diagrams, ii) shortest path distance using the underlying network and iii) ontology-based distance. We employ clustering with these metrics used separately and in pairwise combinations. We propose a novel bi-level optimization approach together with an evolutionary algorithm for informative combination of distance metrics. We compare the enrichment of the obtained clusters between the solutions and with expert knowledge. We calculate the number of Gene and Disease Ontology terms discovered by different solutions as a measure of cluster quality. Our results show that combining distance metrics can improve clustering accuracy, based on the comparison with expert-provided clusters. Also, the performance of specific combinations of distance functions depends on the clustering depth (number of clusters). By employing bi-level optimization approach we evaluated relative importance of distance functions and we found that indeed the order by which they are combined affects clustering performance. Next, with the enrichment analysis of clustering results we found that both hierarchical and bi-level clustering schemes discovered more Gene and Disease Ontology terms than expert-provided clusters for the same knowledge repository. Moreover, bi-level clustering found more enriched terms than the best hierarchical clustering solution for three distinct distance metric combinations in three different instances of disease maps.ConclusionsIn this work we examined the impact of different distance functions on clustering of a visual biomedical knowledge repository. We found that combining distance functions may be beneficial for clustering, and improve exploration of such repositories. We proposed bi-level optimization to evaluate the importance of order by which the distance functions are combined. Both combination and order of these functions affected clustering quality and knowledge recognition in the considered benchmarks. We propose that multiple dimensions can be utilized simultaneously for visual knowledge exploration.

Project description:BackgroundHealthcare research funders may undertake various roles to facilitate implementation of research findings. Their ability to enact such roles depends on several factors, knowledge of implementation being one essential requirement. However, previous studies do not assess the type or level of knowledge about implementation that research funders possess. This paper therefore presents findings from a qualitative, inductive study of the implementation knowledge of research funders. Three aspects of this knowledge are explored, namely how research funders define implementation, their level of self-assessed implementation knowledge and the factors influencing their self-assessment of implementation knowledge.MethodsResearch funders (n?=?18) were purposefully selected from a sample of research funding organisations in Sweden (n?=?10). In-depth semi-structured interviews were conducted, recorded and transcribed verbatim. An inductive method using a systematic coding procedure was employed to derive the findings.ResultsThe research funders defined implementation as either an outcome or a process, with the majority believing that implementation of healthcare research results demands a process, although its complexity varied in the research funders' view. They perceived their own level of implementation knowledge as either limited or substantial, with a majority regarding it as limited. Clinical research experience, clinical experience and task relevance were singled out as the clearest factors affecting the self-assessment of their own implementation knowledge.ConclusionsThis study, the first to focus on implementation knowledge of research funders, demonstrates that they are a category of policy-makers who may possess knowledge, based on their previous professional experience, that is comparable to some important findings from implementation research. Consequently, the findings not only pinpoint the relevance of professional experience, but also reveal a lack of awareness and knowledge of the results of implementation research among research funders in charge of healthcare research.

Dataset Information

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

Publications

DES-TOMATO: A Knowledge Exploration System Focused On Tomato Species.

Similar Datasets

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