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dc.contributor.authorTamaddoni-Nezhad, A.en
dc.contributor.authorChaleil, R.en
dc.contributor.authorKakas, Antonis C.en
dc.contributor.authorMuggleton, S.en
dc.creatorTamaddoni-Nezhad, A.en
dc.creatorChaleil, R.en
dc.creatorKakas, Antonis C.en
dc.creatorMuggleton, S.en
dc.description.abstractThis paper describes the use of a mixture of abduction and induction for the temporal modelling of the effects of toxins in metabolic networks. Background knowledge is used which describes network topology and functional classes of enzymes. This background knowledge, which represents the present state of understanding, is incomplete. In order to overcome this incompleteness hypotheses are considered which consist of a mixture of specific inhibitions of enzymes (ground facts) together with general (non-ground) rules which predict classes of enzymes likely to be inhibited by the toxin. The foreground examples were derived from in vivo experiments involving NMR analysis of time-varying metabolite concentrations in rat urine following injections of toxin. Hypotheses about inhibition are built using the Inductive Logic Programming system Progo15.0 and predictive accuracy is assessed for both the ground and the non-ground cases. © 2005 IEEE.en
dc.sourceProceedings - ICMLA 2005: Fourth International Conference on Machine Learning and Applicationsen
dc.sourceICMLA 2005: 4th International Conference on Machine Learning and Applicationsen
dc.subjectMathematical modelsen
dc.subjectNetwork topologiesen
dc.subjectMedical computingen
dc.subjectBiomedical engineeringen
dc.subjectLearning systemsen
dc.subjectKnowledge engineeringen
dc.subjectInductive Logic Programmingen
dc.subjectLearning modelsen
dc.subjectMetabolic networksen
dc.subjectToxic materialsen
dc.titleAbduction and induction for learning models of inhibition in metabolic networksen
dc.description.endingpage238 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied SciencesΤμήμα Πληροφορικής / Department of Computer Science
dc.type.uhtypeConference Objecten
dc.description.notes<p>Sponsors: California State Universityen
dc.description.notesBakersfield and Association for Machine Learning and Applicationsen
dc.description.notesConference code: 69275en
dc.description.notesCited By :3</p>en
dc.contributor.orcidKakas, Antonis C. [0000-0001-6773-3944]

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