Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language

Hoyt, Charles Tapley; Domingo‐Fernándéz, Daniel; Aldisi, Rana; Xu, Lingling; Kolpeja, Kristian; Spalek, Sandra; Wollert, Esther; Bachman, John A.; Gyori, Benjamin M.; Greene, Patrick; Hofmann‐Apitius, Martin

doi:10.1101/536409

Cited by 7 publications

(12 citation statements)

References 57 publications

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“…Section editors achieved a first selection of 148 papers based on titles and abstracts. After a second review of this set of papers, including full text reviews, a selection of 15 candidate best papers was established 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 . Five reviewers reviewed these pre-selected papers to best four best papers 4 5 6 7 .…”

Section: Resultsmentioning

confidence: 99%

“…The rapid accumulation of new biomedical literature not only causes curated knowledge graphs (KGs) to become outdated and incomplete, but also makes manual curation impractical and unsustainable. Hoyt et al, [11] have developed two workflows to address this issue: the first for re-curating KGs to control syntactic and semantic quality, and the second for rationally enriching KGs through the manually revision of automatically extracted relations for the nodes with low information density. They applied these approaches to the KGs of the NeuroMMSig inventory.…”

Section: Semantic Resources Design Visualization and Curationmentioning

confidence: 99%

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Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Dhombres

Charlet

2020

Yearb Med Inform

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Objective: To select, present, and summarize the best papers in the field of Knowledge Representation and Management (KRM) published in 2019. Methods: A comprehensive and standardized review of the biomedical informatics literature was performed to select the most interesting papers of KRM published in 2019, based on PubMed and ISI Web Of Knowledge queries. Results: Four best papers were selected among 1,189 publications retrieved, following the usual International Medical Informatics Association Yearbook reviewing process. In 2019, research areas covered by pre-selected papers were represented by the design of semantic resources (methods, visualization, curation) and the application of semantic representations for the integration/enrichment of biomedical data. Besides new ontologies and sound methodological guidance to rethink knowledge bases design, we observed large scale applications, promising results for phenotypes characterization, semantic-aware machine learning solutions for biomedical data analysis, and semantic provenance information representations for scientific reproducibility evaluation. Conclusion: In the KRM selection for 2019, research on knowledge representation demonstrated significant contributions both in the design and in the application of semantic resources. Semantic representations serve a great variety of applications across many medical domains, with actionable results.

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Section: Resultsmentioning

confidence: 99%

Section: Semantic Resources Design Visualization and Curationmentioning

confidence: 99%

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Dhombres

Charlet

2020

Yearb Med Inform

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“…To the best of our knowledge, this is the first attempt to compare human and machine-curated disease models and examine how the choice of different query constraints in machine approaches can affect disease modeling. Hoyt et al (2019) manually evaluated 2989 statements generated by INDRA using REACH and Sparser readers containing studied genes from MEDLINE abstracts and PubMed Central full-text articles, following which 30.7% of statements were marked as correct, 48.6% required manual correction, and 20.7% could not be corrected. The criterion for correctness was that "all" aspects of the statement, including the subject and object entities, relationship type, phosphorylation, and other post-translational modifications, were extracted to the same extent as careful manual curation could.…”

Section: Related Workmentioning

confidence: 99%

“…The most common error was wrong name entity recognition. Other common errors were the improper assignment of the subject and object, semantic incorrectness due to the presence of a negation word, and errors arising from evidence that did not actually include relations between the subject and object entities [11]. Allen et al (2015) showed that the DRUM system (Deep Reader for Understanding Mechanisms, a version of the general-purpose TRIPS NLP system customized for extraction of molecular mechanisms from biomedical text) has performance (precision and recall) close to human experts in extracting the molecular mechanisms from text, and it was the best performing system among those evaluated.…”

Section: Related Workmentioning

confidence: 99%

“…Curation is the extraction of knowledge from unstructured data into a structured, computable form [9]. Molecular mechanisms can be extracted from biomedical knowledge resources by manual or automated curation [10,11]. Manual curation consists of the synthesis and integration of information from the literature, large-scale projects, and databases [9] and represents the gold standard for information extraction in biomedical research [12].…”

mentioning

confidence: 99%

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Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

Glavaški

Velicki

2021

BioData Mining

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Background Biomedical knowledge is dispersed in scientific literature and is growing constantly. Curation is the extraction of knowledge from unstructured data into a computable form and could be done manually or automatically. Hypertrophic cardiomyopathy (HCM) is the most common inherited cardiac disease, with genotype–phenotype associations still incompletely understood. We compared human- and machine-curated HCM molecular mechanisms’ models and examined the performance of different machine approaches for that task. Results We created six models representing HCM molecular mechanisms using different approaches and made them publicly available, analyzed them as networks, and tried to explain the models’ differences by the analysis of factors that affect the quality of machine-curated models (query constraints and reading systems’ performance). A result of this work is also the Interactive HCM map, the only publicly available knowledge resource dedicated to HCM. Sizes and topological parameters of the networks differed notably, and a low consensus was found in terms of centrality measures between networks. Consensus about the most important nodes was achieved only with respect to one element (calcium). Models with a reduced level of noise were generated and cooperatively working elements were detected. REACH and TRIPS reading systems showed much higher accuracy than Sparser, but at the cost of extraction performance. TRIPS proved to be the best single reading system for text segments about HCM, in terms of the compromise between accuracy and extraction performance. Conclusions Different approaches in curation can produce models of the same disease with diverse characteristics, and they give rise to utterly different conclusions in subsequent analysis. The final purpose of the model should direct the choice of curation techniques. Manual curation represents the gold standard for information extraction in biomedical research and is most suitable when only high-quality elements for models are required. Automated curation provides more substance, but high level of noise is expected. Different curation strategies can reduce the level of human input needed. Biomedical knowledge would benefit overwhelmingly, especially as to its rapid growth, if computers were to be able to assist in analysis on a larger scale.

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The role of metadata in reproducible computational research

et al. 2021

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Summary Reproducible computational research (RCR) is the keystone of the scientific method for in silico analyses, packaging the transformation of raw data to published results. In addition to its role in research integrity, improving the reproducibility of scientific studies can accelerate evaluation and reuse. This potential and wide support for the FAIR principles have motivated interest in metadata standards supporting reproducibility. Metadata provide context and provenance to raw data and methods and are essential to both discovery and validation. Despite this shared connection with scientific data, few studies have explicitly described how metadata enable reproducible computational research. This review employs a functional content analysis to identify metadata standards that support reproducibility across an analytic stack consisting of input data, tools, notebooks, pipelines, and publications. Our review provides background context, explores gaps, and discovers component trends of embeddedness and methodology weight from which we derive recommendations for future work.

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Re-curation and Rational Enrichment of Knowledge Graphs in Biological Expression Language

Cited by 7 publications

References 57 publications

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Design and Use of Semantic Resources: Findings from the Section on Knowledge Representation and Management of the 2020 International Medical Informatics Association Yearbook

Humans and machines in biomedical knowledge curation: hypertrophic cardiomyopathy molecular mechanisms’ representation

The role of metadata in reproducible computational research

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