Name
Title | ||
|---|---|---|
Module identification from heterogeneous biological data using multiobjective evolutionary algorithms |
Abstract | ||
|---|---|---|
This paper addresses the problem of identifying gene modules on the basis of different types of biological data such as gene expression and protein-protein interaction data. Given one or several genes of interest, the aim is to find a group of genes—containing the prespecified genes—that are maximally similar with respect to all data types and sets under consideration. While existing studies follow an aggregation approach to tackle the problem of data integration in module identification, we here propose a multiobjective evolutionary method that provides several advantages: (i) no overall similarity measure needs to be defined, (ii) the interactions and conflicts between the data sets can be explored, and (iii) arbitrary data types can be integrated. The usefulness of the presented approach is demonstrated on different biological scenarios, also in comparison to standard clustering. |
Year | DOI | Venue |
|---|---|---|
2006 | 10.1007/11844297_58 | PPSN |
Keywords | Field | DocType |
multiobjective evolutionary algorithm,aggregation approach,data type,arbitrary data type,different biological scenario,data integration,biological data,gene expression,heterogeneous biological data,different type,protein-protein interaction data,module identification,protein protein interaction,data integrity | Data integration,Information integration,Biological data,Data set,Evolutionary algorithm,Similarity measure,Computer science,Data type,Artificial intelligence,Cluster analysis,Machine learning | Conference |
Volume | ISSN | ISBN |
4193 | 0302-9743 | 3-540-38990-3 |
Citations | PageRank | References |
1 | 0.35 | 9 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Calonder | 1 | 1490 | 54.95 |
| a stefan bleuler a | 2 | 795 | 35.85 |
| Eckart Zitzler | 3 | 4678 | 291.01 |