Reconstruction quality of a biological network when its constituting elements are partially observed

International audience Unravelling regulatory regulations between biological entities is of utmost importance to understand the functioning of living organisms. As the number of available samples is often very low (often less than one hundred), inference methods are frequently performed on a subset...

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Bibliographic Details
Main Authors: Picheny, Victor, Vandel, Jimmy, Vignes, Matthieu, Villa-Vialaneix, Nathalie
Other Authors: Mathématiques et Informatique Appliquées de Toulouse (MIAT), Institut National de la Recherche Agronomique (INRA)
Format: Conference Object
Language:English
Published: HAL CCSD 2014
Subjects:
[MATH.MATH-ST]Mathematics [math]/Statistics [math.ST]
[STAT.TH]Statistics [stat]/Statistics Theory [stat.TH]
Iceland
Online Access:https://hal.archives-ouvertes.fr/hal-00982758
https://hal.archives-ouvertes.fr/hal-00982758/document
https://hal.archives-ouvertes.fr/hal-00982758/file/picheny_etal_IASTAT2014.pdf
Description
Summary:International audience Unravelling regulatory regulations between biological entities is of utmost importance to understand the functioning of living organisms. As the number of available samples is often very low (often less than one hundred), inference methods are frequently performed on a subset of variables which make sense in the mechanisms under study. Classical remedies are either data driven (e.g., differentially expressed genes) or knowledge driven (e.g., using ontology information). However, whatever the chosen solution, important variables are very likely missed by the selection process, which is the issue at stake in the present paper.

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