185 research outputs found
Mapping data elements to terminological resources for integrating biomedical data sources
BACKGROUND: Data integration is a crucial task in the biomedical domain and integrating data sources is one approach to integrating data. Data elements (DEs) in particular play an important role in data integration. We combine schema- and instance-based approaches to mapping DEs to terminological resources in order to facilitate data sources integration. METHODS: We extracted DEs from eleven disparate biomedical sources. We compared these DEs to concepts and/or terms in biomedical controlled vocabularies and to reference DEs. We also exploited DE values to disambiguate underspecified DEs and to identify additional mappings. RESULTS: 82.5% of the 474 DEs studied are mapped to entries of a terminological resource and 74.7% of the whole set can be associated with reference DEs. Only 6.6% of the DEs had values that could be semantically typed. CONCLUSION: Our study suggests that the integration of biomedical sources can be achieved automatically with limited precision and largely facilitated by mapping DEs to terminological resources
Measurements of KL Branching Fractions and the CP Violation Parameter |eta+-|
We present new measurements of the six largest branching fractions of the KL
using data collected in 1997 by the KTeV experiment (E832) at Fermilab. The
results are B(KL -> pi e nu) = 0.4067 +- 0.0011 B(KL -> pi mu nu) = 0.2701 +-
0.0009 B(KL -> pi+ pi- pi0) = 0.1252 +- 0.0007 B(KL -> pi0 pi0 pi0) = 0.1945 +-
0.0018 B(KL -> pi+ pi-) = (1.975 +- 0.012)E-3, and B(KL -> pi0 pi0) = (0.865 +-
0.010)E-3, where statistical and systematic errors have been summed in
quadrature. We also determine the CP violation parameter |eta+-| to be (2.228
+- 0.010)E-3. Several of these results are not in good agreement with averages
of previous measurements.Comment: Submitted to Phys. Rev. D; 20 pages, 22 figure
PhenoHM: human–mouse comparative phenome–genome server
PhenoHM is a human–mouse comparative phenome–genome server that facilitates cross-species identification of genes associated with orthologous phenotypes (http://phenome.cchmc.org; full open access, login not required). Combining and extrapolating the knowledge about the roles of individual gene functions in the determination of phenotype across multiple organisms improves our understanding of gene function in normal and perturbed states and offers the opportunity to complement biologically the rapidly expanding strategies in comparative genomics. The Mammalian Phenotype Ontology (MPO), a structured vocabulary of phenotype terms that leverages observations encompassing the consequences of mouse gene knockout studies, is a principal component of mouse phenotype knowledge source. On the other hand, the Unified Medical Language System (UMLS) is a composite collection of various human-centered biomedical terminologies. In the present study, we mapped terms reciprocally from the MPO to human disease concepts such as clinical findings from the UMLS and clinical phenotypes from the Online Mendelian Inheritance in Man knowledgebase. By cross-mapping mouse–human phenotype terms, extracting implicated genes and extrapolating phenotype-gene associations between species PhenoHM provides a resource that enables rapid identification of genes that trigger similar outcomes in human and mouse and facilitates identification of potentially novel disease causal genes. The PhenoHM server can be accessed freely at http://phenome.cchmc.org
A transversal approach to predict gene product networks from ontology-based similarity
<p>Abstract</p> <p>Background</p> <p>Interpretation of transcriptomic data is usually made through a "standard" approach which consists in clustering the genes according to their expression patterns and exploiting Gene Ontology (GO) annotations within each expression cluster. This approach makes it difficult to underline functional relationships between gene products that belong to different expression clusters. To address this issue, we propose a transversal analysis that aims to predict functional networks based on a combination of GO processes and data expression.</p> <p>Results</p> <p>The transversal approach presented in this paper consists in computing the semantic similarity between gene products in a Vector Space Model. Through a weighting scheme over the annotations, we take into account the representativity of the terms that annotate a gene product. Comparing annotation vectors results in a matrix of gene product similarities. Combined with expression data, the matrix is displayed as a set of functional gene networks. The transversal approach was applied to 186 genes related to the enterocyte differentiation stages. This approach resulted in 18 functional networks proved to be biologically relevant. These results were compared with those obtained through a standard approach and with an approach based on information content similarity.</p> <p>Conclusion</p> <p>Complementary to the standard approach, the transversal approach offers new insight into the cellular mechanisms and reveals new research hypotheses by combining gene product networks based on semantic similarity, and data expression.</p
TargetMine, an Integrated Data Warehouse for Candidate Gene Prioritisation and Target Discovery
Prioritising candidate genes for further experimental characterisation is a
non-trivial challenge in drug discovery and biomedical research in general. An
integrated approach that combines results from multiple data types is best
suited for optimal target selection. We developed TargetMine, a data warehouse
for efficient target prioritisation. TargetMine utilises the InterMine
framework, with new data models such as protein-DNA interactions integrated in a
novel way. It enables complicated searches that are difficult to perform with
existing tools and it also offers integration of custom annotations and in-house
experimental data. We proposed an objective protocol for target prioritisation
using TargetMine and set up a benchmarking procedure to evaluate its
performance. The results show that the protocol can identify known
disease-associated genes with high precision and coverage. A demonstration
version of TargetMine is available at http://targetmine.nibio.go.jp/
Phenomenological model for the Kbar N --> K Xi reaction
A phenomenological model for the Kbar N --> K Xi reaction is suggested. The
model includes s and u channel exchanges by Lambda, Sigma, Sigma(1385), and
Lambda(1520) and s channel exchanges by above-threshold hyperonic resonances.
Explicit expression for the propagator for a particle with spin 7/2 is
presented. High-mass and high-spin resonances play a significant role in the
process. We deal with the whole set of existing experimental data on the cross
sections and polarizations in the energy range from the threshold to 2.8 GeV in
the center-of-mass system and reach a good agreement with experiments.
Applications of the model to other elementary reactions of Xi production and to
Xi hypernuclear spectroscopy are briefly discussed.Comment: Published version; minor change
Long-term kidney function recovery and mortality after COVID-19-associated acute kidney injury: An international multi-centre observational cohort study
Background: While acute kidney injury (AKI) is a common complication in COVID-19, data on post-AKI kidney function recovery and the clinical factors associated with poor kidney function recovery is lacking. Methods: A retrospective multi-centre observational cohort study comprising 12,891 hospitalized patients aged 18 years or older with a diagnosis of SARS-CoV-2 infection confirmed by polymerase chain reaction from 1 January 2020 to 10 September 2020, and with at least one serum creatinine value 1–365 days prior to admission. Mortality and serum creatinine values were obtained up to 10 September 2021. Findings: Advanced age (HR 2.77, 95%CI 2.53–3.04, p < 0.0001), severe COVID-19 (HR 2.91, 95%CI 2.03–4.17, p < 0.0001), severe AKI (KDIGO stage 3: HR 4.22, 95%CI 3.55–5.00, p < 0.0001), and ischemic heart disease (HR 1.26, 95%CI 1.14–1.39, p < 0.0001) were associated with worse mortality outcomes. AKI severity (KDIGO stage 3: HR 0.41, 95%CI 0.37–0.46, p < 0.0001) was associated with worse kidney function recovery, whereas remdesivir use (HR 1.34, 95%CI 1.17–1.54, p < 0.0001) was associated with better kidney function recovery. In a subset of patients without chronic kidney disease, advanced age (HR 1.38, 95%CI 1.20–1.58, p < 0.0001), male sex (HR 1.67, 95%CI 1.45–1.93, p < 0.0001), severe AKI (KDIGO stage 3: HR 11.68, 95%CI 9.80–13.91, p < 0.0001), and hypertension (HR 1.22, 95%CI 1.10–1.36, p = 0.0002) were associated with post-AKI kidney function impairment. Furthermore, patients with COVID-19-associated AKI had significant and persistent elevations of baseline serum creatinine 125% or more at 180 days (RR 1.49, 95%CI 1.32–1.67) and 365 days (RR 1.54, 95%CI 1.21–1.96) compared to COVID-19 patients with no AKI. Interpretation: COVID-19-associated AKI was associated with higher mortality, and severe COVID-19-associated AKI was associated with worse long-term post-AKI kidney function recovery. Funding: Authors are supported by various funders, with full details stated in the acknowledgement section
International comparisons of laboratory values from the 4CE collaborative to predict COVID-19 mortality
Given the growing number of prediction algorithms developed to predict COVID-19 mortality, we evaluated the transportability of a mortality prediction algorithm using a multi-national network of healthcare systems. We predicted COVID-19 mortality using baseline commonly measured laboratory values and standard demographic and clinical covariates across healthcare systems, countries, and continents. Specifically, we trained a Cox regression model with nine measured laboratory test values, standard demographics at admission, and comorbidity burden pre-admission. These models were compared at site, country, and continent level. Of the 39,969 hospitalized patients with COVID-19 (68.6% male), 5717 (14.3%) died. In the Cox model, age, albumin, AST, creatine, CRP, and white blood cell count are most predictive of mortality. The baseline covariates are more predictive of mortality during the early days of COVID-19 hospitalization. Models trained at healthcare systems with larger cohort size largely retain good transportability performance when porting to different sites. The combination of routine laboratory test values at admission along with basic demographic features can predict mortality in patients hospitalized with COVID-19. Importantly, this potentially deployable model differs from prior work by demonstrating not only consistent performance but also reliable transportability across healthcare systems in the US and Europe, highlighting the generalizability of this model and the overall approach
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