Towards an integrative and context-sensitive approach to in silico disease modelling

Matej Orešič (Corresponding Author), Peddinti V. Gopalacharyulu, Erno Lindfors, Catherine Bounsaythip, Ilkka Karanta, Mikko Hiirsalmi, Lauri Seitsonen, Paula Silvonen

    Research output: Contribution to journalArticleProfessional

    Abstract

    Historically, the scientific methods applied to biological problems have largely been limited due to the fact that it has been difficult to collect the data. Today, these scientific methods are challenged by the 'omics' revolution, which are empowering us with the ability to collect large amounts of data in parallel from a particular system. However, the development of efficient tools to exploit this data within the context of biological systems has been much slower. Due to this mismatch the knowledge acquisition in life sciences is actually increasingly difficult and new information technology solutions are needed to resolve this problem.
    Original languageEnglish
    Pages (from-to)25 - 26
    Number of pages2
    JournalERCIM News
    Issue number60
    Publication statusPublished - 2005
    MoE publication typeNot Eligible

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    Cite this

    Orešič, M., Gopalacharyulu, P. V., Lindfors, E., Bounsaythip, C., Karanta, I., Hiirsalmi, M., ... Silvonen, P. (2005). Towards an integrative and context-sensitive approach to in silico disease modelling. ERCIM News, (60), 25 - 26.
    Orešič, Matej ; Gopalacharyulu, Peddinti V. ; Lindfors, Erno ; Bounsaythip, Catherine ; Karanta, Ilkka ; Hiirsalmi, Mikko ; Seitsonen, Lauri ; Silvonen, Paula. / Towards an integrative and context-sensitive approach to in silico disease modelling. In: ERCIM News. 2005 ; No. 60. pp. 25 - 26.
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    abstract = "Historically, the scientific methods applied to biological problems have largely been limited due to the fact that it has been difficult to collect the data. Today, these scientific methods are challenged by the 'omics' revolution, which are empowering us with the ability to collect large amounts of data in parallel from a particular system. However, the development of efficient tools to exploit this data within the context of biological systems has been much slower. Due to this mismatch the knowledge acquisition in life sciences is actually increasingly difficult and new information technology solutions are needed to resolve this problem.",
    author = "Matej Orešič and Gopalacharyulu, {Peddinti V.} and Erno Lindfors and Catherine Bounsaythip and Ilkka Karanta and Mikko Hiirsalmi and Lauri Seitsonen and Paula Silvonen",
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    Orešič, M, Gopalacharyulu, PV, Lindfors, E, Bounsaythip, C, Karanta, I, Hiirsalmi, M, Seitsonen, L & Silvonen, P 2005, 'Towards an integrative and context-sensitive approach to in silico disease modelling', ERCIM News, no. 60, pp. 25 - 26.

    Towards an integrative and context-sensitive approach to in silico disease modelling. / Orešič, Matej (Corresponding Author); Gopalacharyulu, Peddinti V.; Lindfors, Erno; Bounsaythip, Catherine; Karanta, Ilkka; Hiirsalmi, Mikko; Seitsonen, Lauri; Silvonen, Paula.

    In: ERCIM News, No. 60, 2005, p. 25 - 26.

    Research output: Contribution to journalArticleProfessional

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    T1 - Towards an integrative and context-sensitive approach to in silico disease modelling

    AU - Orešič, Matej

    AU - Gopalacharyulu, Peddinti V.

    AU - Lindfors, Erno

    AU - Bounsaythip, Catherine

    AU - Karanta, Ilkka

    AU - Hiirsalmi, Mikko

    AU - Seitsonen, Lauri

    AU - Silvonen, Paula

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    AB - Historically, the scientific methods applied to biological problems have largely been limited due to the fact that it has been difficult to collect the data. Today, these scientific methods are challenged by the 'omics' revolution, which are empowering us with the ability to collect large amounts of data in parallel from a particular system. However, the development of efficient tools to exploit this data within the context of biological systems has been much slower. Due to this mismatch the knowledge acquisition in life sciences is actually increasingly difficult and new information technology solutions are needed to resolve this problem.

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    Orešič M, Gopalacharyulu PV, Lindfors E, Bounsaythip C, Karanta I, Hiirsalmi M et al. Towards an integrative and context-sensitive approach to in silico disease modelling. ERCIM News. 2005;(60):25 - 26.