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| Content Provider | PubMed Central |
|---|---|
| Author | Saithong, Treenut Bumee, Somkid Liamwirat, Chalothorn Meechai, Asawin |
| Editor | Semsey, Szabolcs |
| Copyright Year | 2012 |
| Abstract | Boolean-based method, despite of its simplicity, would be a more attractive approach for inferring a network from high-throughput expression data if its effectiveness has not been limited by high false positive prediction. In this study, we explored factors that could simply be adjusted to improve the accuracy of inferring networks. Our work focused on the analysis of the effects of discretisation methods, biological constraints, and stringency of Boolean function assignment on the performance of Boolean network, including accuracy, precision, specificity and sensitivity, using three sets of microarray time-series data. The study showed that biological constraints have pivotal influence on the network performance over the other factors. It can reduce the variation in network performance resulting from the arbitrary selection of discretisation methods and stringency settings. We also presented the master Boolean network as an approach to establish the unique solution for Boolean analysis. The information acquired from the analysis was summarised and deployed as a general guideline for an efficient use of Boolean-based method in the network inference. In the end, we provided an example of the use of such a guideline in the study of Arabidopsis circadian clock genetic network from which much interesting biological information can be inferred. |
| Related Links | http://dx.doi.org/10.1371/journal.pone.0030232 |
| Starting Page | 30232 |
| File Format | |
| ISSN | 19326203 |
| e-ISSN | 19326203 |
| Journal | PLoS ONE |
| Issue Number | 1 |
| Volume Number | 7 |
| Language | English |
| Publisher | Public Library of Science |
| Publisher Date | 2012-01-01 |
| Access Restriction | Open |
| Rights Holder | Public Library of Science |
| Subject Keyword | Biochemistry, Genetics and Molecular Biology(all) Agricultural and Biological Sciences(all) Medicine(all) Research in Higher Education |
| Content Type | Text |
| Resource Type | Article |
| Subject | Multidisciplinary |
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