Parallel distributed processing: explorations in the microstructure of cognition, vol. 1: foundations
Bayesian Methods for Elucidating Genetic Regulatory Networks
IEEE Intelligent Systems
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
| Hi-index | 0.00 |
With the advent of new genomic platforms there is the potential for data mining of genomic profiles associated with specific subclasses of disease. Many groups have focused on the identification of genes associated with these subclasses. Fewer groups have taken this analysis a stage further to identify potential associations between biomolecules to determine hypothetical inferred biological interaction networks (e.g. gene regulatory networks) associated with a given condition (termed the interactome). Here we present an artificial neural network based approach using the back propagation algorithm to explore associations between genes in hypothetical inferred pathways, by iteratively predicting the level of expression of each gene with the others, with respect to the genes associated with metastatic risk in breast cancer based on the publicly available van't Veer data set [1]. We demonstrate that we can identify a subset of genes that is strongly associated with others within the metastatic system. Many of these interactions are strongly representative of likely biological interactions and the interacting genes are known to be associated with metastatic disease.