Gene Ontology: Looking backwards and forwards

Department of Molecular and Cell Biology, University of California, 539 Life Sciences Addition, Berkeley, CA 94720-3200, USA.
Genome biology (Impact Factor: 10.47). 02/2005; 6(1):103. DOI: 10.1186/gb-2004-6-1-103
Source: PubMed

ABSTRACT The Gene Ontology consortium began six years ago with a group of scientists who decided to connect our data by sharing the same language for describing it. Its most significant achievement lies in uniting many independent biological database efforts into a cooperative force.

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Available from: Suzanna Elaine Lewis, Aug 20, 2015
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    • "Limited as these axes may seem, the work has had a tremendous impact on the analysis of the many different genome scale data types, and new analysis approaches based on ontological descriptions continue to be developed. However , initially GO was developed non-formally and eventually the need to formalise it became obvious [27] [28] [29]. "
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    ABSTRACT: Over the last decade the biological sciences have been widely embracing Systems Biology and its various data integration approaches to discover new knowledge. Molecular Systems Biology aims to develop hypotheses based on integrated, or modelled data. These hypotheses can be subsequently used to design new experiments for testing, leading to an improved understanding of the biology; a more accurate model of the biological system and therefore an improved ability to develop hypotheses. During the same period the biosciences have also eagerly taken up the emerging Semantic Web as evidenced by the dedicated exploitation of semantic web technologies for data integration and sharing in the Life Sciences. We describe how these two approaches merged in Semantic Systems Biology: a data integration and analysis approach complementary to model-based Systems Biology. Semantic Systems Biology augments the integration and sharing of knowledge, and opens new avenues for computational support in quality checking and automated reasoning, and to develop new, testable hypotheses.
    New Biotechnology 11/2012; 30(3). DOI:10.1016/j.nbt.2012.11.008 · 2.11 Impact Factor
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    • "Bio-ontologies may be used for other endeavours, such as for instance managing and accessing data in the first place (that is, even before they are circulated beyond the laboratory where they are originally produced). 3 The Gene Ontology, as many within the OBO Consortium, is specifically devoted to representing the biological knowledge underlying the reuse of data within new research contexts: in other words, it defines the ontology that researchers need to share to successfully draw new inferences from existing data sets (Ashburner et al. 2000; Lewis 2004; Renear and Palmer 2009). At the same time, the Gene Ontology is constantly modified depending on the state of research and the interests of their users, and the mechanisms through which it is updated make this bio-ontology particularly helpful in disseminating data for the purpose of discovery (Leonelli et al. 2011). "
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    ABSTRACT: Knowledge-making practices in biology are being strongly affected by the availability of data on an unprecedented scale, the insistence on systemic approaches and growing reliance on bioinformatics and digital infrastructures. What role does theory play within data-intensive science, and what does that tell us about scientific theories in general? To answer these questions, I focus on Open Biomedical Ontologies, digital classification tools that have become crucial to sharing results across research contexts in the biological and biomedical sciences, and argue that they constitute an example of classificatory theory. This form of theorizing emerges from classification practices in conjunction with experimental know-how and expresses the knowledge underpinning the analysis and interpretation of data disseminated online.
    International Studies in the Philosophy of Science 03/2012; 26(1). DOI:10.1080/02698595.2012.653119
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    • "An exemplary case is the Gene Ontology Consortium, which was instrumental to the development and current success of the GO as a classification tool. The GO was not started through appositely allocated funds, but rather through an informal network of collaboration among the curators of prominent community databases such as FlyBase, Mouse Genome Informatics and Saccharomyces Genome database (Lewis 2004). These researchers were aware that the problems emerging in relation to data classification could not be solved by single-handed initiatives. "
    Handbook of Genetics and Society:, 01/2009;
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