Name
Title | ||
|---|---|---|
Hierarchical clustering combining numerical and biological similarities for gene expression data classification. |
Abstract | ||
|---|---|---|
High throughput data analysis is a challenging problem due to the vast amount of available data. A major concern is to develop algorithms that provide accurate numerical predictions and biologically relevant results. A wide variety of tools exist in the literature using biological knowledge to evaluate analysis results. Only recently, some works have included biological knowledge inside the analysis process improving the prediction results. |
Year | DOI | Venue |
|---|---|---|
2013 | 10.1109/EMBC.2013.6609567 | EMBC |
Keywords | Field | DocType |
biological knowledge,pattern clustering,classical numerical similarity,prediction results,genetics,hierarchical systems,genomics,microarray classification,data analysis,hierarchical clustering process,gene list analysis tool,knowledge integration scheme,knowledge acquisition,high throughput data analysis,biological similarity,similarity measure,gene expression data classification,bioinformatics,data integration,metagenes,databases,algorithm design and analysis,clustering algorithms,prediction algorithms | Hierarchical clustering,Data integration,Data mining,Algorithm design,Knowledge integration,Similarity measure,Computer science,Artificial intelligence,Data classification,Cluster analysis,Knowledge acquisition,Machine learning | Conference |
Volume | ISSN | Citations |
2013 | 1557-170X | 0 |
PageRank | References | Authors |
0.34 | 4 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mattia Bosio | 1 | 1 | 2.07 |
| Philippe Salembier | 2 | 603 | 87.65 |
| Pau Bellot | 3 | 9 | 1.52 |
| Albert Oliveras-Vergés | 4 | 9 | 2.22 |