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dc.contributor.authorHu, Y
dc.contributor.authorKasabov, N
dc.contributor.authorLiang, W
dc.date.accessioned2014-03-21T00:38:35Z
dc.date.available2014-03-21T00:38:35Z
dc.date.copyright2014
dc.date.issued2014-03-21
dc.identifier.citationSpringer Handbook of Bio-/Neuroinformatics (2014), pp 533-553
dc.identifier.urihttp://hdl.handle.net/10292/6986
dc.description.abstractPersonalised modelling offers a new and effective approach for the study in pattern recognition and knowledge discovery, especially for biomedical applications. The created models are more useful and informative for analysing and evaluating an individual data object for a given problem. Such models are also expected to achieve a higher degree of accuracy of prediction of outcome or classification than conventional systems and methodologies. Motivated by the concept of personalised medicine and utilising transductive reasoning, personalised modelling was recently proposed as a new method for knowledge discovery in biomedical applications. Personalised modelling aims to create a unique computational diagnostic or prognostic model for an individual. Here we introduce an integrated method for personalised modelling that applies global optimisation of variables (features) and an appropriate size of neighbourhood to create an accurate personalised model for an individual. This method creates an integrated computational system that combines different information processing techniques, applied at different stages of data analysis, e.g. feature selection, classification, discovering the interaction of genes, outcome prediction, personalised profiling and visualisation, etc. It allows for adaptation, monitoring and improvement of an individual’s model and leads to improved accuracy and unique personalised profiling that could be used for personalised treatment and personalised drug design.
dc.publisherSpringer
dc.rightsAn author may self-archive an author-created version of his/her article on his/her own website and or in his/her institutional repository. He/she may also deposit this version on his/her funder’s or funder’s designated repository at the funder’s request or as a result of a legal obligation, provided it is not made publicly available until 12 months after official publication. He/ she may not use the publisher's PDF version, which is posted on www.springerlink.com, for the purpose of self-archiving or deposit. Furthermore, the author may only post his/her version provided acknowledgement is given to the original source of publication and a link is inserted to the published article on Springer's website. The link must be accompanied by the following text: "The final publication is available at www.springerlink.com”. (Please also see Publisher’s Version and Citation).
dc.titlePersonalised information modelling technologies for personalised medicine
dc.typeChapter in Book
dc.rights.accessrightsOpenAccess
dc.identifier.doi10.1007/978-3-642-30574-0_33
aut.relation.endpage553
aut.relation.startpage533
pubs.elements-id94370


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