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
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;
Conference_Titel :
Complex, Intelligent and Software Intensive Systems, 2008. CISIS 2008. International Conference on
Conference_Location :
Barcelona
Print_ISBN :
978-0-7695-3109-0
DOI :
10.1109/CISIS.2008.147