The European Bioinformatics Institute's data resources: towards systems biology

Brooksbank, Cath; Cameron, Graham; Thornton, Janet M.

doi:10.1093/nar/gki026

Cited by 90 publications

(57 citation statements)

References 42 publications

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“…This kind of use of the concept of function occurs in the Celltype Ontology (Bard et al, 2005) (CL) and the Ontology of Chemical Entities of Biological Interest (Brooksbank et al, 2005) (ChEBI). We will only explore the Celltype Ontology, but the same argument can be applied to ChEBI.…”

Section: Identifying Implicit Functions and Processesmentioning

confidence: 99%

“…Although the GO provides a short definition for its notion of molecular function, an in-depth analysis is not provided. Further uses of the notion of function appear in the Chemical Entities of Biological Interest (ChEBI) Ontology (Brooksbank et al, 2005) and in the Celltype (CL) Ontology (Bard et al, 2005), equally without a strong theoretical basis concerning functions.…”

Section: Introductionmentioning

confidence: 99%

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A top-level ontology of functions and its application in the Open Biomedical Ontologies

et al. 2006

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Motivation: A clear understanding of functions in biology is a key component in accurate modelling of molecular, cellular and organismal biology. Using the existing biomedical ontologies it has been impossible to capture the complexity of the community's knowledge about biological functions. Results: We present here a top-level ontological framework for representing knowledge about biological functions. This framework lends greater accuracy, power and expressiveness to biomedical ontologies by providing a means to capture existing functional knowledge in a more formal manner. An initial major application of the ontology of functions is the provision of a principled way in which to curate functional knowledge and annotations in biomedical ontologies. Further potential applications include the facilitation of ontology interoperability and automated reasoning. A major advantage of the proposed implementation is that it is an extension to existing biomedical ontologies, and can be applied without substantial changes to these domain ontologies. Availability: The Ontology of Functions (OF) can be downloaded in OWL format from http://onto.eva.mpg.de/. Additionally, a UML profile and supplementary information and guides for using the OF can be accessed from the same website.

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Section: Identifying Implicit Functions and Processesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A top-level ontology of functions and its application in the Open Biomedical Ontologies

et al. 2006

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“…In bioinformatics, the journal Nucleic Acids Research features an annual web Server Issue and an annual Database Issue. Recent articles include those on European Bioinformatics Institute's (EBI's) resources [19], the National Center for Biotechnology Information's (NCBI's) resources [20], the molecular biology database collection [21], the bioinformatics links directory [22], and the Online Bioinformatics Resources Collection (OBRC) [23]. Philippi and Köhler [24], [25] discuss the many problems impeding the semantic integration of these life science databases and ontologies.…”

Section: Life Sciences Web and Gridmentioning

confidence: 99%

DOORS to the Semantic Web and Grid With a PORTAL for Biomedical Computing

Taswell

2008

IEEE Trans. Inform. Technol. Biomed.

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Abstract-The semantic web remains in the early stages of development. It has not yet achieved the goals envisioned by its founders as a pervasive web of distributed knowledge and intelligence. Success will be attained when a dynamic synergism can be created between people and a sufficient number of infrastructure systems and tools for the semantic web in analogy with those for the original web. The domain name system (DNS), web browsers, and the benefits of publishing web pages motivated many people to register domain names and publish web sites on the original web. An analogous resource label system, semantic search applications, and the benefits of collaborative semantic networks will motivate people to register resource labels and publish resource descriptions on the semantic web. The Domain Ontology Oriented Resource System (DOORS) and Problem Oriented Registry of Tags and Labels (PORTAL) are proposed as infrastructure systems for resource metadata within a paradigm that can serve as a bridge between the original web and the semantic web. Registers domain names while DNS publishes domain addresses with mapping of names to addresses for the original web. Analogously, POR-TAL registers resource labels and tags while DOORS publishes resource locations and descriptions with mapping of labels to locations for the semantic web. BioPORT is proposed as a prototype PORTAL registry specific for the problem domain of biomedical computing.

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“…On the other hand, statistical machine-learning approaches can be applied on a much larger scale. The increasing amount of information linking chemical and biological data (Austin et al, 2004;Brooksbank et al, 2005;Kanehisa et al, 2006;Wishart et al, 2006) has provided the required input for these machine-learning tools. Machinelearning approaches are computationally efficient, and they do not necessarily require three-dimensional structural information.…”

Section: Introductionmentioning

confidence: 99%

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

et al. 2007

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Motivation: Identifying protein enzymatic or pharmacological activities are important areas of research in biology and chemistry. Biological and chemical databases are increasingly being populated with linkages between protein sequences and chemical structures.There is now sufficient information to apply machine-learning techniques to predict interactions between chemicals and proteins at a genome scale. Current machine-learning techniques use as input either protein sequences and structures or chemical information. We propose here a method to infer protein-chemical interactions using heterogeneous input consisting of both protein sequence and chemical information. Results: Our method relies on expressing proteins and chemicals with a common cheminformatics representation. We demonstrate our approach by predicting whether proteins can catalyze reactions not present in training sets. We also predict whether a given drug can bind a target, in the absence of prior binding information for that drug and target. Such predictions cannot be made with current machine-learning techniques requiring binding information for individual reactions or individual targets. Availability and Contact: For questions, paper reprints, please contact Jean-Loup Faulon at jfaulon@sandia.gov. Additional information on the signature molecular descriptor and codes can be downloaded at:

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The European Bioinformatics Institute's data resources: towards systems biology

Cited by 90 publications

References 42 publications

A top-level ontology of functions and its application in the Open Biomedical Ontologies

A top-level ontology of functions and its application in the Open Biomedical Ontologies

DOORS to the Semantic Web and Grid With a PORTAL for Biomedical Computing

Genome scale enzyme–metabolite and drug–target interaction predictions using the signature molecular descriptor

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