Reference : Computational modeling under uncertainty: challenges and opportunities
Parts of books : Contribution to collective works
Engineering, computing & technology : Multidisciplinary, general & others
Computational modeling under uncertainty: challenges and opportunities
Gomez-Cabrero, David []
Tegner, Jesper []
Geris, Liesbet mailto [Université de Liège - ULiège > Département d'aérospatiale et mécanique > Génie biomécanique >]
Modeling under uncertainty: a computational modeling approach
[en] Computational Biology has increasingly become an important tool for biomedical and translational research. In particular, when generating novel hypothesis despite fundamental uncertainties in data and mechanistic understanding of biological processes underpinning diseases. While in the present book, we have reviewed the necessary background and existing novel methodologies that set the basis for dealing with uncertainty, there are still many “grey”, or less well-defined, areas of investigations offering both challenges and opportunities. This final chapter in the book provides some reflections on those areas, namely: (1) the need for novel robust mathematical and statistical methodologies to generate hypothesis under uncertainty; (2) the challenge of aligning those methodologies in a context that requires larger computational resources; (3) the accessibility of modeling tools for less mathematical literate researchers; and (4) the integration of models with –omics data and its application in clinical environments.
FP7 ; 279100 - BRIDGE - Biomimetic process design for tissue regeneration: from bench to bedside via in silico modelling

File(s) associated to this reference

Fulltext file(s):

Open access
C18_Computational modeling_future_2015_03_v24_FINAL.pdfAuthor preprint141.62 kBView/Open

Bookmark and Share SFX Query

All documents in ORBi are protected by a user license.