| dc.contributor.author | Pang, Wei | |
| dc.contributor.author | Coghill, George M | |
| dc.date.accessioned | 2014-12-09T10:03:01Z | |
| dc.date.available | 2014-12-09T10:03:01Z | |
| dc.date.issued | 2015-02 | |
| dc.identifier.citation | Pang , W & Coghill , G M 2015 , ' QML-AiNet : an immune network approach to learning qualitative differential equation models ' , Applied Soft Computing , vol. 27 , pp. 148-157 . https://doi.org/10.1016/j.asoc.2014.11.008 | en |
| dc.identifier.issn | 1568-4946 | |
| dc.identifier.other | PURE: 43926660 | |
| dc.identifier.other | PURE UUID: 2a1c6a70-50e5-4fdf-b38c-9332c9e4c764 | |
| dc.identifier.other | Scopus: 84912553137 | |
| dc.identifier.other | ORCID: /0000-0002-1761-6659/work/59923321 | |
| dc.identifier.other | ORCID: /0000-0002-2047-8277/work/63561573 | |
| dc.identifier.uri | http://hdl.handle.net/2164/4091 | |
| dc.description | Acknowledgements WP and GMC are supported by the CRISP project (Combinatorial Responses in Stress Pathways) funded by the BBSRC (award reference: BB/F00513X/1) under the Systems Approaches to Biological Research (SABR) Initiative. | en |
| dc.format.extent | 10 | |
| dc.language.iso | eng | |
| dc.relation.ispartof | Applied Soft Computing | en |
| dc.rights | This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. http://creativecommons.org/licenses/by/3.0/ | en |
| dc.subject | qualitative model learning | en |
| dc.subject | artificial immune systems | en |
| dc.subject | immune network approach | en |
| dc.subject | compartmental models | en |
| dc.subject | qualitative reasoning | en |
| dc.subject | qualitative differential equation | en |
| dc.subject | QA75 Electronic computers. Computer science | en |
| dc.subject | Biotechnology and Biological Sciences Research Council (BBSRC) | en |
| dc.subject | BB/F00513X/1 | en |
| dc.subject.lcc | QA75 | en |
| dc.title | QML-AiNet : an immune network approach to learning qualitative differential equation models | en |
| dc.type | Journal article | en |
| dc.contributor.institution | University of Aberdeen.Computing Science | en |
| dc.description.status | Peer reviewed | en |
| dc.description.version | Publisher PDF | en |
| dc.identifier.doi | https://doi.org/10.1016/j.asoc.2014.11.008 | |
