Name
Abstract | ||
|---|---|---|
Boolean Network (BN) is a popular and simple mathematical model which receives a lot of attention because of its capacity to reveal the behavior of a genetic regulatory network. Furthermore, observability, as a significant network feature, plays a critical role in understanding the underlying mechanism behind a genetic network. Several studies have been done on observability of BNs and complex networks. However, observability of (singleton or cyclic) attractor cycles, which can be regarded as a biomarker of disease, has not yet been fully addressed in the literature. Therefore, it becomes an urgent issue which deserves a detailed study. In this work, we first formulate a novel definition of singleton or cyclic attractor observability in BNs. Then we develop an efficient method to solve the captured problem and the complexity is of O(P(m)n), where P is the maximum period of cyclic attractor, m is the number of attractor and n is the number of genes in the network. Furthermore, we validate our model with computational experiments and show that our proposed method is effective and efficient for the captured observability problem. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1109/BIBM.2018.8621334 | PROCEEDINGS 2018 IEEE INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICINE (BIBM) |
Keywords | Field | DocType |
Boolean Networks, Cyclic Attractor, Attractor Observability | Boolean network,Attractor,Observability,Computer science,Theoretical computer science,Artificial intelligence,Complex network,Singleton,Genetic network,Machine learning | Conference |
ISSN | Citations | PageRank |
2156-1125 | 0 | 0.34 |
References | Authors | |
0 | 8 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yushan Qiu | 1 | 20 | 6.28 |
| Yulong Huang | 2 | 7 | 5.59 |
| Shaobo Tan | 3 | 7 | 5.59 |
| Dongqi Li | 4 | 7 | 5.59 |
| Ada Chaeli Van Der Zijp-Tan | 5 | 4 | 2.17 |
| Ada Fong | 6 | 3 | 1.47 |
| Glen M. Borchert | 7 | 26 | 8.17 |
| Jingshan Huang | 8 | 94 | 23.27 |