Title |
Learning Delayed Influences of Biological Systems
|
---|---|
Published in |
Frontiers in Bioengineering and Biotechnology, January 2015
|
DOI | 10.3389/fbioe.2014.00081 |
Pubmed ID | |
Authors |
Tony Ribeiro, Morgan Magnin, Katsumi Inoue, Chiaki Sakama |
Abstract |
Boolean networks are widely used model to represent gene interactions and global dynamical behavior of gene regulatory networks. To understand the memory effect involved in some interactions between biological components, it is necessary to include delayed influences in the model. In this paper, we present a logical method to learn such models from sequences of gene expression data. This method analyzes each sequence one by one to iteratively construct a Boolean network that captures the dynamics of these observations. To illustrate the merits of this approach, we apply it to learning real data from bioinformatic literature. Using data from the yeast cell cycle, we give experimental results and show the scalability of the method. We show empirically that using this method we can handle millions of observations and successfully capture delayed influences of Boolean networks. |
X Demographics
Geographical breakdown
Country | Count | As % |
---|---|---|
France | 1 | 33% |
United States | 1 | 33% |
Switzerland | 1 | 33% |
Demographic breakdown
Type | Count | As % |
---|---|---|
Members of the public | 3 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
---|---|---|
Unknown | 17 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
---|---|---|
Student > Ph. D. Student | 4 | 24% |
Researcher | 3 | 18% |
Lecturer | 1 | 6% |
Student > Master | 1 | 6% |
Student > Bachelor | 1 | 6% |
Other | 2 | 12% |
Unknown | 5 | 29% |
Readers by discipline | Count | As % |
---|---|---|
Computer Science | 4 | 24% |
Engineering | 2 | 12% |
Medicine and Dentistry | 2 | 12% |
Nursing and Health Professions | 1 | 6% |
Biochemistry, Genetics and Molecular Biology | 1 | 6% |
Other | 1 | 6% |
Unknown | 6 | 35% |