Abstract
Reverse engineering of gene regulatory networks from microarray time series data has been a challenging problem due to the limit of available data. In this paper, a new approach is proposed based on the concept of transfer entropy. Using this information theoretic measure, causal relations between pairs of genes are assessed to draw a causal network. A heuristic rule is then applied to differentiate direct and indirect causality. Simulation on a synthetic network showed that the transfer entropy can identify both linear and nonlinear causality. Application of the method in a biological data identified many causal interactions with biological information supports.
Original language | English (US) |
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Title of host publication | Proceedings - Twentieth IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 |
Pages | 383-388 |
Number of pages | 6 |
DOIs | |
State | Published - 2007 |
Externally published | Yes |
Event | 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 - Maribor, Slovenia Duration: Jun 20 2007 → Jun 22 2007 |
Other
Other | 20th IEEE International Symposium on Computer-Based Medical Systems, CBMS'07 |
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Country | Slovenia |
City | Maribor |
Period | 06/20/07 → 06/22/07 |
ASJC Scopus subject areas
- Engineering(all)