DocumentCode
2681562
Title
Diagnostic and Therapy Prediction in Breast Cancer by Consistent Knowledge Discovery
Author
Doncescu, Andrei ; Richard, Gilles ; Farmer, Muhamed
Author_Institution
LAAS-CNRS, Univ. of Toulouse, Toulouse
fYear
2008
fDate
4-7 March 2008
Firstpage
286
Lastpage
292
Abstract
Cells are able to use the bottom-up information (genome versus metabolic pathway) but also top-down environment/gene mutation. Our approach considers the information emergency from the sub-systems to the whole system. In these conditions an unified analytical model is very difficult to build up. Therefore an abstract model can be very helpful for prognosis diagnostic in medical science. We enhance the pertinence analysis of the information which is primordially to find out relationship between experimental data and biological knowledge.
Keywords
cancer; data mining; genetics; medical computing; patient diagnosis; breast cancer; diagnostic prediction; gene mutation; knowledge discovery; medical science; pertinence analysis; prognosis diagnostic; therapy prediction; top-down environment; Bioinformatics; Breast cancer; Clustering algorithms; Clustering methods; Hidden Markov models; Learning systems; Logic programming; Medical diagnostic imaging; Medical treatment; Testing; breast cancer; fuzzy logic; inductive logic programming;
fLanguage
English
Publisher
ieee
Conference_Titel
Complex, Intelligent and Software Intensive Systems, 2008. CISIS 2008. International Conference on
Conference_Location
Barcelona
Print_ISBN
978-0-7695-3109-0
Type
conf
DOI
10.1109/CISIS.2008.147
Filename
4606694
Link To Document