Name
Abstract | ||
|---|---|---|
A path diagram relates observed, pairwise, variable correlations to a functional structure which describes the hypothesized causal relations between the variables. Here we combine path diagrams, heuristics and evolutionary search into a system which seeks to improve existing gene regulatory models. Our evaluation shows that once a correct model has been identified it receives a lower prediction error compared to incorrect models, indicating the overall feasibility of this approach. However, with smaller samples the observed correlations gradually become more misleading, and the evolutionary search increasingly converges on suboptimal models. Future work will incorporate publicly available sources of experimentally verified biological facts to computationally suggest model modifications which might improve the model's fitness. |
Year | DOI | Venue |
|---|---|---|
2007 | 10.1007/978-3-540-71783-6_11 | EvoBIO |
Keywords | Field | DocType |
improved path diagram,suboptimal model,biological fact,correct model,incorrect model,available source,evolutionary search,path diagram,regulatory model,observed correlation,model modification,prediction error | Pairwise comparison,Mean squared prediction error,Path coefficient,Causal relations,Computer science,Diagram,Heuristics,Artificial intelligence,Gene regulatory network,Machine learning | Conference |
Volume | ISSN | Citations |
4447 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 7 | 6 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Kim Laurio | 1 | 23 | 3.24 |
| Thomas Svensson | 2 | 5 | 2.47 |
| Mats Jirstrand | 3 | 206 | 23.75 |
| Patric Nilsson | 4 | 7 | 1.80 |
| Jonas Gamalielsson | 5 | 81 | 13.11 |
| Björn Olsson | 6 | 82 | 22.82 |