dc.contributor.author | Tamaddoni-Nezhad, A. | en |
dc.contributor.author | Kakas, Antonis C. | en |
dc.contributor.author | Muggleton, S. | en |
dc.contributor.author | Pazos, F. | en |
dc.contributor.editor | Camacho, Ricardo J. | en |
dc.contributor.editor | King R. | en |
dc.contributor.editor | Srinivasan A. | en |
dc.creator | Tamaddoni-Nezhad, A. | en |
dc.creator | Kakas, Antonis C. | en |
dc.creator | Muggleton, S. | en |
dc.creator | Pazos, F. | en |
dc.date.accessioned | 2019-11-13T10:42:27Z | |
dc.date.available | 2019-11-13T10:42:27Z | |
dc.date.issued | 2004 | |
dc.identifier.uri | http://gnosis.library.ucy.ac.cy/handle/7/55052 | |
dc.description.abstract | In this paper, we study how a logical form of scientific modelling that integrates together abduction and induction can be used to understand the functional class of unknown enzymes or inhibitors. We show how we can model, within Abductive Logic Programming (ALP), inhibition in metabolic pathways and use abduction to generate facts about inhibition of enzymes by a particular toxin (e.g. Hydrazine) given the underlying metabolic pathway and observations about the concentration of metabolites. These ground facts, together with biochemical background information, can then be generalised by ILP to generate rules about the inhibition by Hydrazine thus enriching further our model. In particular, using Progol 5.0 where the processes of abduction and inductive generalization are integrated enables us to learn such general rules. Experimental results on modelling in this way the effect of Hydrazine in a real metabolic pathway are presented. © Springer-Verlag Berlin Heidelberg 2004. | en |
dc.source | Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science) | en |
dc.source | 14th International Conference ILP 2004: Inductive Logic Programming | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-22944452109&partnerID=40&md5=4fc1cc359aa6f856c66623b7a05afcb0 | |
dc.subject | Reaction kinetics | en |
dc.subject | Enzyme inhibition | en |
dc.subject | Medical computing | en |
dc.subject | Nuclear magnetic resonance | en |
dc.subject | Enzymes | en |
dc.subject | Metabolic pathways | en |
dc.subject | Metabolism | en |
dc.subject | Hydrazine | en |
dc.subject | Logic programming | en |
dc.subject | Abduction | en |
dc.subject | Abductive logic programming | en |
dc.subject | Inductive logic programming (ILP) | en |
dc.subject | Living systems studies | en |
dc.subject | Abductive logic programming (ALP) | en |
dc.subject | Background information | en |
dc.subject | Functional class | en |
dc.subject | Induction | en |
dc.subject | Logical forms | en |
dc.title | Modelling inhibition in metabolic pathways through abduction and induction | en |
dc.type | info:eu-repo/semantics/conferenceObject | |
dc.description.volume | 3194 | |
dc.description.startingpage | 305 | |
dc.description.endingpage | 322 | |
dc.author.faculty | 002 Σχολή Θετικών και Εφαρμοσμένων Επιστημών / Faculty of Pure and Applied Sciences | |
dc.author.department | Τμήμα Πληροφορικής / Department of Computer Science | |
dc.type.uhtype | Conference Object | en |
dc.description.notes | <p>Sponsors: Fundacao para a Ciencia e a Tecnologia, FCT, Portugal | en |
dc.description.notes | DEEC (FEUP), Portugal | en |
dc.description.notes | FEUP, Portugal | en |
dc.description.notes | Conference code: 65301 | en |
dc.description.notes | Cited By :11</p> | en |
dc.contributor.orcid | Kakas, Antonis C. [0000-0001-6773-3944] | |
dc.gnosis.orcid | 0000-0001-6773-3944 | |