Name
Abstract | ||
|---|---|---|
This paper illustrates how the Quadratic Assignment Problem (QAP) is used as a mathematical model that helps to produce a visualization of microarray data, based on the relationships between the objects (genes or samples). The visualization method can also incorporate the result of a clustering algorithm to facilitate the process of data analysis. Specifically, we show the integration with a graph-based clustering algorithm that outperforms the results against other benchmarks, namely k-means and self-organizing maps. Even though the application uses gene expression data, the method is general and only requires a similarity function being defined between pairs of objects. The microarray dataset is based on the budding yeast (S. cerevisiae). It is composed of 79 samples taken from different experiments and 2, 467 genes. The proposed method delivers an automatically generated visualization of the microarray dataset based on the integration of the relationships coming from similarity measures, a clustering result and a graph structure. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-76931-6_14 | ACAL |
Keywords | Field | DocType |
data analysis,integrated qap-based approach,similarity function,gene expression data,gene expression similarity,clustering result,visualization method,clustering algorithm,graph-based clustering algorithm,microarray data,microarray dataset,mathematical model,gene expression,k means,quadratic assignment problem | Memetic algorithm,Data mining,Graph,Visualization,Computer science,Quadratic assignment problem,Microarray analysis techniques,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
4828 | 0302-9743 | 3-540-76930-7 |
Citations | PageRank | References |
3 | 0.49 | 6 |
Authors | ||
4 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Mario Inostroza-Ponta | 1 | 41 | 11.08 |
| Alexandre Mendes | 2 | 163 | 18.23 |
| Regina Berretta | 3 | 49 | 11.60 |
| Pablo Moscato | 4 | 334 | 37.27 |