Projects / Programmes source: ARIS

Knowledge discovery methods for functional genomics

Research activity

Code Science Field Subfield
2.07.07  Engineering sciences and technologies  Computer science and informatics  Intelligent systems - software 

Code Science Field
P176  Natural sciences and mathematics  Artificial intelligence 
B110  Biomedical sciences  Bioinformatics, medical informatics, biomathematics biometrics 
knowledge discovery from data bases, bioinformatics, functional genomics, data mining, machine learning, knowledge-based data analysis
Evaluation (rules)
source: COBISS
Researchers (6)
no. Code Name and surname Research area Role Period No. of publicationsNo. of publications
1.  02275  PhD Ivan Bratko  Computer science and informatics  Researcher  2002 - 2004  741 
2.  16324  PhD Janez Demšar  Computer science and informatics  Head  2002 - 2004  340 
3.  20225  PhD Aleks Jakulin  Computer science and informatics  Researcher  2002 - 2004  38 
4.  21352  PhD Peter Juvan  Human reproduction  Researcher  2002 - 2004  163 
5.  15754  PhD Dorian Šuc  Computer science and informatics  Researcher  2002 - 2004  43 
6.  12536  PhD Blaž Zupan  Computer science and informatics  Researcher  2002 - 2004  530 
Organisations (1)
no. Code Research organisation City Registration number No. of publicationsNo. of publications
1.  1539  University of Ljubljana, Faculty of Computer and Information Science  Ljubljana  1627023  15,972 
The goal of this project is to develop AI-based tools and techniques, including machine learning and data mining, for intelligent data analysis and knowledge discovery in functional genomics. Special emphasis will be put on the methods that effectively and efficiently explore both the existing domain knowledge and data, and that are able to present the discovered knowledge in a comprehensible and explainable way. While we aim at the development of methods to be applicable throughout different areas and problem domains of functional genomics, in collaboration with our research partners from Baylor College of Medicine, Houston, USA, a specific evaluation of the methods will be done through their experimental application to several analysis and gene function discovery tasks from genetic data of a soil amoeba Dictyostelium.
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