Six questions on the construction of ontologies in biomedicine

(Report assembled for the Workshop of the AMIA Working Group on Formal Biomedical Knowledge Representation in connection with AMIA Symposium, Washington DC, 2005.) Best practices in ontology building for biomedicine have been frequently discussed in recent years. However there is a range of seemingly disparate views represented by experts in the field. These views not only reflect the different uses to which ontologies are put, but also the experiences and disciplinary background of these experts themselves. We asked six questions related to biomedical ontologies to what we believe is a representative sample of ontologists in the biomedical field and came to a number conclusions which we believe can help provide an insight into the practical problems which ontology builders face today

A 3-D diamondoid MOF catalyst based on in situ generated [Cu(L)2] N-heterocyclic carbene (NHC) linkers: hydroboration of CO(2)

Includes supplementary informationA new MOF, [Zn4O{Cu(L)2}2] (1), with a 4-fold interpenetrated 3D diamondoid structure was synthesised from in situ generated [Cu(L)2] NHC linkers. MOF 1 possesses tetrahedral Zn4O nodes, which are unusually coordinated by four pairs of carboxylates from four [Cu(L)2] linkers, and 14 A 1-D pore channels lined with [Cu(L)2] moieties that catalyse the hydroboration of CO2.Alexandre Burgun, Rachel S. Crees, Marcus L. Cole, Christian J. Doonan and Christopher J. Sumb

Site-specific metal and ligand substitutions in a microporous Mn(2+)-based metal-organic framework

First published online 16 Feb 2016The precise tuning of the structural and chem. features of microporous metal-org. frameworks (MOFs) is a crucial endeavour for developing materials with properties that are suitable for specific applications. In recent times, techniques for prepg. frameworks consisting of mixed-metal or ligand compns. have emerged. However, controlled spatial organization of the components within these structures at the mol. scale is a difficult challenge, particularly when species possessing similar geometries or chem. properties are used. Here, we describe the synthesis of mixed-metal and ligand variants possessing the Mn3L3 (Mn-MOF-1; H2L = bis(4-(4'-carboxyphenyl)-3,5-dimethylpyrazolyl)methane) structure type. In the case of mixed-ligand synthesis using a mixt. of L and its trifluoromethyl-functionalised deriv. (H2L' = bis(4-(4'-carboxyphenyl)-3,5-di(trifluoromethyl)pyrazolyl)methane), a mixed-ligand product in which the L' species predominanantly occupies the pillar sites lining the pores is obtained. Meanwhile, post-synthetic metal exchange of the parent Mn3L3 compd. using Fe2+ or Fe3+ ions results in cation exchange at the carboxylate clusters and metalation at the pillar bispyrazolate sites. The results demonstrate the versatility of the Mn3L3 structure type toward both metal and ligand substitutions, and the potential utility of site-specific functionalisations in achieving even greater precision in the tuning of MOFs. [on SciFinder(R)]Michael Huxley, Campbell J. Coghlan, Alexandre Burgun, Andrew Tarzia, Kenji Sumida, Christopher J. Sumby, and Christian J. Doona

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

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

Some reactions of azides with diynyl-bis(phosphine)ruthenium-cyclopentadienyl complexes

Abstract not availableMichael I. Bruce, Alexandre Burgun, Jonathan George, Brian K. Nicholson, Christian R. Parker, Brian W. Skelton, Nancy Scoleri, Christopher J. Sumby, Natasha N. Zaitsev

War of Ontology Worlds: Mathematics, Computer Code, or Esperanto?

The use of structured knowledge representations—ontologies and terminologies—has become standard in biomedicine. Definitions of ontologies vary widely, as do the values and philosophies that underlie them. In seeking to make these views explicit, we conducted and summarized interviews with a dozen leading ontologists. Their views clustered into three broad perspectives that we summarize as mathematics, computer code, and Esperanto. Ontology as mathematics puts the ultimate premium on rigor and logic, symmetry and consistency of representation across scientific subfields, and the inclusion of only established, non-contradictory knowledge. Ontology as computer code focuses on utility and cultivates diversity, fitting ontologies to their purpose. Like computer languages C++, Prolog, and HTML, the code perspective holds that diverse applications warrant custom designed ontologies. Ontology as Esperanto focuses on facilitating cross-disciplinary communication, knowledge cross-referencing, and computation across datasets from diverse communities. We show how these views align with classical divides in science and suggest how a synthesis of their concerns could strengthen the next generation of biomedical ontologies

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/

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