Elements of information theory
Elements of information theory
Linear Modeling of Genetic Networks from Experimental Data
Proceedings of the Eighth International Conference on Intelligent Systems for Molecular Biology
| Hi-index | 0.00 |
In this paper, the authors describe a gene network reverse engineering method, which employs mutual information to infer the connections between the genes. Given the expression profile of the genes, determined for different conditions, the method calculates the similarity matrix corresponding to the mutual information between each pair of genes. The approximated matrix can be subsequently refined by using a data processing inequality and an analytically estimated threshold for the statistical significance of mutual information. The authors have used the proposed method to reconstruct a network of 2041 gene transcription factors, measured over 79 human tissues. The numerical results show that the connectivity of the transcription factors network is characterized by a scale free distribution, with an exponent of the power law between 1.5 and 2. The power law for the connectivity distribution implies that the network is extremely heterogeneous; i.e., its topology is dominated by a few highly connected genes, which link the rest of the loosely-connected genes to the system.