Name
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. |
Year | DOI | Venue |
|---|---|---|
2008 | 10.1109/CISIS.2008.147 | CISIS |
Keywords | Field | DocType |
abstract model,gene mutation,bottom-up information,breast cancer,experimental data,unified analytical model,biological knowledge,medical science,top-down environment,therapy prediction,pertinence analysis,information emergency,consistent knowledge discovery,top down,cancer,logic programming,biology,knowledge discovery,genetics,clustering algorithms,bioinformatics,bottom up,hidden markov models,testing,cognition,fuzzy logic,data mining,metabolic pathway | Inductive logic programming,Distance measurement,Experimental data,Breast cancer,Computer science,Knowledge extraction,Artificial intelligence,Cognition,Machine learning,Cancer | Conference |
Citations | PageRank | References |
0 | 0.34 | 4 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Doncescu, A. | 1 | 86 | 25.70 |
| Gilles Richard | 2 | 175 | 20.88 |
| Muhamed Farmer | 3 | 0 | 0.34 |