Name
Abstract | ||
|---|---|---|
Current analyses of co-expressed genes are often based on global approaches such as clustering or bi-clustering. An alternative way is to employ local methods and search for patterns--sets of genes displaying specific expression properties in a set of situations. The main bottleneck of this type of analysis is twofold--computational costs and an overwhelming number of candidate patterns which can hardly be further exploited. A timely application of background knowledge available in literature databases, biological ontologies and other sources can help to focus on the most plausible patterns only. The paper proposes, implements and tests a flexible constraint-based framework that enables the effective mining and representation of meaningful over-expression patterns representing intrinsic associations among genes and biological situations. The framework can be simultaneously applied to a wide spectrum of genomic data and we demonstrate that it allows to generate new biological hypotheses with clinical implications. |
Year | Venue | Field |
|---|---|---|
2008 | In Silico Biology | Data mining,Bottleneck,Biology,Biological Ontologies,Gene ontology,Knowledge extraction,Bioinformatics,Cluster analysis |
DocType | Volume | Issue |
Journal | 8 | 2 |
ISSN | Citations | PageRank |
1386-6338 | 3 | 0.43 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jirí Klémal | 1 | 35 | 5.87 |
| Sylvain Blachon | 2 | 82 | 5.07 |
| Arnaud Soulet | 3 | 241 | 28.18 |
| Bruno Crémilleux | 4 | 373 | 34.98 |
| Olivier Gandrillon | 5 | 176 | 12.53 |