Cancer profiles by affinity propagation
The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. A well know breast cancer case series was used to compare the results of the affinity...
| Main Authors: | , , , , |
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| Format: | Conference or Workshop Item |
| Published: |
2008
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| Online Access: | https://eprints.nottingham.ac.uk/28142/ |
| _version_ | 1848793516397821952 |
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| author | Ambrogi, Federico Raimondi, Elena Soria, Daniele Boracchi, Patrizia Biganzoli, Elia M. |
| author_facet | Ambrogi, Federico Raimondi, Elena Soria, Daniele Boracchi, Patrizia Biganzoli, Elia M. |
| author_sort | Ambrogi, Federico |
| building | Nottingham Research Data Repository |
| collection | Online Access |
| description | The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered.
A well know breast cancer case series was used to compare the results of the affinity propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters.
Results from affinity propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters. |
| first_indexed | 2025-11-14T19:01:32Z |
| format | Conference or Workshop Item |
| id | nottingham-28142 |
| institution | University of Nottingham Malaysia Campus |
| institution_category | Local University |
| last_indexed | 2025-11-14T19:01:32Z |
| publishDate | 2008 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | nottingham-281422020-05-04T20:27:30Z https://eprints.nottingham.ac.uk/28142/ Cancer profiles by affinity propagation Ambrogi, Federico Raimondi, Elena Soria, Daniele Boracchi, Patrizia Biganzoli, Elia M. The affinity propagation algorithm is applied to a problem of breast cancer subtyping using traditional biologic markers. The algorithm provides a procedure to determine the number of profiles to be considered. A well know breast cancer case series was used to compare the results of the affinity propagation with the results obtained with standard algorithms and indexes for the optimal choice of the number of clusters. Results from affinity propagation are consistent with the results already obtained having the advantage of providing an indication about the number of clusters. 2008 Conference or Workshop Item PeerReviewed Ambrogi, Federico, Raimondi, Elena, Soria, Daniele, Boracchi, Patrizia and Biganzoli, Elia M. (2008) Cancer profiles by affinity propagation. In: Seventh International Conference on Machine Learning and Applications, 2008. ICMLA'08., 11-13 Dec. 2008, San Diego, California. http://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4725044 |
| spellingShingle | Ambrogi, Federico Raimondi, Elena Soria, Daniele Boracchi, Patrizia Biganzoli, Elia M. Cancer profiles by affinity propagation |
| title | Cancer profiles by affinity propagation |
| title_full | Cancer profiles by affinity propagation |
| title_fullStr | Cancer profiles by affinity propagation |
| title_full_unstemmed | Cancer profiles by affinity propagation |
| title_short | Cancer profiles by affinity propagation |
| title_sort | cancer profiles by affinity propagation |
| url | https://eprints.nottingham.ac.uk/28142/ https://eprints.nottingham.ac.uk/28142/ |