Projects per year
Abstract
This paper describes the methodology of providing multiprobability predictions for proteomic mass spectrometry data. The methodology is based on a newly developed machine learning framework called Venn machines. They allow us to output a valid probability interval. We apply this methodology to mass spectrometry data sets in order to predict the diagnosis of heart disease and early diagnoses of ovarian cancer. The experiments show that probability intervals are valid and narrow. In addition, probability intervals were compared with the output of a corresponding probability predictor.
Original language | English |
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Title of host publication | Artificial Intelligence Applications and Innovations - AIAI 2012 International Workshops: AIAB, AIeIA, CISE, COPA, IIVC, ISQL, MHDW, and WADTMB, Halkidiki, Greece, September 27-30, 2012, Proceedings, Part II |
Editors | Lazaros Iliadis, Ilias Maglogiannis, Harris Papadopoulos, Kostas Karatzas, Spyros Sioutas |
Publisher | Springer |
Volume | 382 |
ISBN (Electronic) | 978-3-642-33411-5 |
Publication status | Published - 2012 |
Publication series
Name | IFIP Advances in Information and Communication Technology |
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Projects
- 2 Finished
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Development of New Venn Prediction Methods for Osteoporosis Risk Assessment
Gammerman, A. (PI) & Vovk, V. (CoI)
Research Promotion Foundation of Cyprus
1/09/11 → 31/08/13
Project: Research
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Living with uninvited guests-comparing palnt and animal responses to endocytic invasions
Jansen , V. A. A. (PI), Gammerman, A. (CoI) & Soloviev, M. (CoI)
Biotechnology&BioSci Research BBSRC
15/04/10 → 14/04/13
Project: Research