Generation of genetic networks from a small number of gene expression patterns under the Boolean network model.
Keywords:
Generation, consistent network
Abstract
There are lots of work for inferring genetic network architectures from state transition tables which correspond to time series of gene expression patterns, using the Boolean network model. Results of those computational experiments suggested that a small number of state transition (INPUT/OUTPUT) pairs are sufficient in order to infer the original Boolean network correctly. Tatsuya AKUTSU, Satoru MIYANO and Satoru KUHARA gave a mathematical proof for this. So there is possibility to devise an algorithm to generate all consistent genetic networks from a small number of gene expression patterns under the Boolean network model.
Downloads
- Article PDF
- TEI XML Kaleidoscope (download in zip)* (Beta by AI)
- Lens* NISO JATS XML (Beta by AI)
- HTML Kaleidoscope* (Beta by AI)
- DBK XML Kaleidoscope (download in zip)* (Beta by AI)
- LaTeX pdf Kaleidoscope* (Beta by AI)
- EPUB Kaleidoscope* (Beta by AI)
- MD Kaleidoscope* (Beta by AI)
- FO Kaleidoscope* (Beta by AI)
- BIB Kaleidoscope* (Beta by AI)
- LaTeX Kaleidoscope* (Beta by AI)
How to Cite
Published
2011-08-15
Issue
Section
License
Copyright (c) 2011 Authors and Global Journals Private Limited
This work is licensed under a Creative Commons Attribution 4.0 International License.