Coefficient of determination in nonlinear signal processing
Signal Processing - Special section on signal processing technologies for short burst wireless communications
Exact performance of error estimators for discrete classifiers
Pattern Recognition
Planning interventions in biological networks
ACM Transactions on Intelligent Systems and Technology (TIST)
Context-specific gene regulatory networks subdivide intrinsic subtypes of breast cancer
DTMBIO '10 Proceedings of the ACM fourth international workshop on Data and text mining in biomedical informatics
SFFS-MR: a floating search strategy for GRNs inference
PRIB'10 Proceedings of the 5th IAPR international conference on Pattern recognition in bioinformatics
Inference of restricted stochastic boolean GRN's by Bayesian error and entropy based criteria
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
Hi-index | 0.00 |
A more complete understanding of the alterations in cellular regulatory and control mechanisms that occur in the various forms of cancer has been one of the central targets of the genomic and proteomic methods that allow surveys of the abundance and/or state of cellular macromolecules. This preference is driven both by the intractability of cancer to generic therapies, assumed to be due to the highly varied molecular etiologies observed in cancer, and by the opportunity to discern and dissect the regulatory and control interactions presented by the highly diverse assortment of perturbations of regulation and control that arise in cancer. Exploiting the opportunities for inference on the regulatory and control connections offered by these revealing system perturbations is fraught with the practical problems that arise from the way biological systems operate. Two classes of regulatory action in biological systems are particularly inimical to inference, convergent regulation, where a variety of regulatory actions result in a common set of control responses (crosstalk), and divergent regulation, where a single regulatory action produces entirely different sets of control responses, depending on cellular context (conditioning). We have constructed a coarse mathematical model of the propagation of regulatory influence in such distributed, context-sensitive regulatory networks that allows a quantitative estimation of the amount of crosstalk and conditioning associated with a candidate regulatory gene taken from a set of genes that have been profiled over a series of samples where the candidate's activity varies.