Literature DB >> 17099942

Path-based systems to guide scientists in the maze of biological data sources.

Sarah Cohen-Boulakia1, Susan Davidson, Christine Froidevaux, Zoé Lacroix, Maria-Esther Vidal.   

Abstract

Fueled by novel technologies capable of producing massive amounts of data for a single experiment, scientists are faced with an explosion of information which must be rapidly analyzed and combined with other data to form hypotheses and create knowledge. Today, numerous biological questions can be answered without entering a wet lab. Scientific protocols designed to answer these questions can be run entirely on a computer. Biological resources are often complementary, focused on different objects and reflecting various experts' points of view. Exploiting the richness and diversity of these resources is crucial for scientists. However, with the increase of resources, scientists have to face the problem of selecting sources and tools when interpreting their data. In this paper, we analyze the way in which biologists express and implement scientific protocols, and we identify the requirements for a system which can guide scientists in constructing protocols to answer new biological questions. We present two such systems, BioNavigation and BioGuide dedicated to help scientists select resources by following suitable paths within the growing network of interconnected biological resources.

Mesh:

Year:  2006        PMID: 17099942     DOI: 10.1142/s0219720006002375

Source DB:  PubMed          Journal:  J Bioinform Comput Biol        ISSN: 0219-7200            Impact factor:   1.122


  3 in total

1.  Supporting retrieval of diverse biomedical data using evidence-aware queries.

Authors:  Eithon Cadag; Peter Tarczy-Hornoch
Journal:  J Biomed Inform       Date:  2010-07-17       Impact factor: 6.317

2.  GenoQuery: a new querying module for functional annotation in a genomic warehouse.

Authors:  Frédéric Lemoine; Bernard Labedan; Christine Froidevaux
Journal:  Bioinformatics       Date:  2008-07-01       Impact factor: 6.937

3.  Learning virulent proteins from integrated query networks.

Authors:  Eithon Cadag; Peter Tarczy-Hornoch; Peter J Myler
Journal:  BMC Bioinformatics       Date:  2012-12-02       Impact factor: 3.169

  3 in total

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