Relational subgroup discovery for descriptive analysis of microarray data

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Volumn 4216 LNBI, Issue , 2006, Pages 86-96

  Trajkovski, Igor ^a   Železný, Filip ^b   Tolar, Jakub ^c   Lavrač, Nada ^a  

^a JO EF STEFAN INSTITUTE   (Slovenia)

^b CZECH TECHNICAL UNIVERSITY IN PRAGUE   (Czech Republic)

^c UNIVERSITY OF MINNESOTA MEDICAL SCHOOL   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

CLASSIFICATION (OF INFORMATION); DATA STRUCTURES; FEATURE EXTRACTION; GENES; INFORMATION ANALYSIS; TUMORS;

ACUTE LYMPHOBLASTIC LEUKEMIA (ALL); GENE ONTOLOGY; MICROARRAY DATA; SUBGROUP DISCOVERY;

MEDICAL COMPUTING;

EID: 33750392184     PISSN: 03029743     EISSN: 16113349     Source Type: Book Series    
DOI: 10.1007/11875741_9     Document Type: Conference Paper

References (13)

1. Clark, P. & Niblett T. (1989). The CN2 induction algorithm. Machine Learning, pages 261-283.
2. Eisen, M. B., Spellman, P. T., Brown, P. O., & Botstein, D. (1998). Cluster analysis and display of genome-wide expression patterns. Proc. Natl. Acad. Sci. USA, 95:25, 14863-14868.
3. Golub, T. R., Slonim, D. K., Tamayo, P., Huard, C., Gaasenbeek, M. et al. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring. Science, 286:5439, 531-537.
4. Kasper, G. et al. (2005). Expression levels of the putative zinc transporter LIV-1 are associated with a better outcome of breast cancer patients. Int J Cancer. 20;117(6):961-73.
5. Khatri, P. et al. (2002). Profiling gene expression using Onto-Express. Genomics, 79, 266-270.
6. Khatri, P. & Draghici S. (2005). Ontological analysis of gene expression data: current tools, limitations, and open problems. Bioinformatics, 21(18):3587-3595
7. Lavrač, N., Železný F. & Flach P. (2002). RSD: Relational subgroup discovery through first-order feature construction. In Proceedings of the 12th International Conference on Inductive Logic Programming, pages 149-165.
8. Mani, A. & Gelmann E.P. (2005). The ubiquitin-proteasome pathway and its role in cancer. Journal of Clinical Oncology. 23:4776-4789
9. Muggleton, S. (1995). Inverse entailment and Progol. New Generation Computing, Special issue on Inductive Logic Programming, 13(3-4):245-286.
10. Ramaswamy, S., Tamayo P., Rifkin R. et al. (2001). Multiclass cancer diagnosis using tumor gene expression signatures, PNAS 98 (26) 15149-15154.
11. Raychaudhuri, S., Schtze,H.S. & Altman,R.B. (2003). Inclusion of textual documentation in the analysis of multidimensional data sets: application to gene expression data. Machine Learn., 52, 119-145
12. Wrobel, S. (1997). An algorithm for multi-relational discovery of subgroups. In Jan Komorowski and Jan Zytkow, editors, Proceedings of the First European Symposion on Principles of Data Mining and Knowledge Discovery, 78-87.
13. Železný, F. & N. Lavrač. (2006). Propositionalization-Based Relational Subgroup Discovery with RSD. Machine Learning 62(1-2):33-63. Springer 2006.