Probabilistic boolean networks

Data processing: artificial intelligence – Neural network

Reexamination Certificate

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Reexamination Certificate

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10354907

ABSTRACT:
Embodiments of the invention encompass methods for modeling of complex systems, which include, but are not limited to gene regulatory networks, biological systems, and the like. Other embodiments of the invention include the development of computational tools for the identification and discovery of potential targets for therapeutic intervention in diseases such as cancer. The embodiments discussed utilize methods that model the potential effect of individual genes on the global dynamical network behavior, both from the view of random gene mutation as well as intervention in order to elicit desired network behavior.

REFERENCES:
patent: 6169981 (2001-01-01), Werbos
patent: 2002/0042786 (2002-04-01), Scarborough et al.
patent: 2002/0046199 (2002-04-01), Scarborough et al.
patent: 2003/0130973 (2003-07-01), Sumner et al.
Learning by probabilistic Boolean networks, Dorigo, M.; Neural Networks, 1994. IEEE World Congress on Computational Intelligence., IEEE International Conference on pp. 887-891 vol. 2 Digital Object Identifier 10.1109/ICNN.1994.374297.
Datta et al., “External control in Markovian genetic regulatory networks,” (to appear in)Machine Learning., pp. 1-25.
Dougherty and Shmulevich, “Mappings between probabilistic Boolean networks,”Signal Processing, 83:799-809, 2003.
Dougherty et al., “Coefficient of determination in nonlinear signal processing,”Signal Processing, 80:2219-2235, 2000.
Hashimoto et al., “Efficient selection of feature sets possessing high coefficients of determination based on incremental determinations,”Signal Processing, 83(4):695-712, 2003.
Kim et al., “Can Markov chain models mimic biological regulation?”J. Biol. Sys., 10:337-357, 2002.
Kim et al., “General nonlinear framework for the analysis of gene interaction via multivariate expression arrays,”J. Biomed. Optics, 5(4):411-424, 2000.
Kim et al., “Multivariate measurement of gene expression relationships,”Genomics, 67:201-209, 2000.
Lähdesmäki et al., “On learning gene regulatory networks under the boolean network system,”Machine Learning, 17(36):1-26, 2002.
Melnik et al., “Block-Median Pyramidal Transform: Analysis and Denoising Applications,”IEEE Transactions on Signal Processing, 49(2):364-372, 2001.
Shmulevich et al., “Control of stationary behavior in probabilistic Boolean networks by means of structural intervention,”J. Biol. Sys., 10:431-445, 2002.
Shmulevich et al., “From Bolean to probabilistic Boolean networks as models of genetic regulatory networks,”Proceedings of the IEEE, 90:1778-1792, 2002.
Shmulevich et al., “Gene pertubation and intervention in probabilistic Boolean networks,”Bioinformatics, 18:1319-1331, 2002.
Shmulevich et al., “Inference of genetic regulatory networks via best-fit extensions,” in:Computational and Statistical Approaches to Genomics, Zhang and Shmulevich (eds.), Chapter 11: pp. 197-210, 2002.
Shmulevich et al., “Probabilistic Boolean networks: a rule-based uncertainty model for gene regulatory networks,”Bioinformatics, 18:261-274, 2002.
Suh et al., “Parallel computation and visualization tools for codetermination analysis of multivariate gene-expression relations,” in:Computational and Statistical Approaches to Genomics, Zhang and Shmulevich (eds.), chapter 13: pp. 227-240, 2002.
Tabus and Astola, “On the use of MDL principle in gene expression prediction,”J. of Applied Signal Processing, 2001(4):297-303, 2001.
Tabus et al., “Normalized maximum likelihood models for Boolean regression with application to prediction and classification in genomics,” in:Computational and Statistical Approaches to Genomics, Zhang and Shmulevich (eds.), Chapter 10: pp. 173-196, 2002.
Watts and Strogatz, “Collective dynamics of ‘small-world’ networks,”Nature, 393(6684):440-442, 1998.
Zhou et al., “Construction of genomic networks using mutual-information clustering and reversible-jump Markov-chain-Monte-Carlo predictor design,”Signal Processing, 83:745-761, 2003.
Fox and Furmanski, “Load balancing loosely synchronous problems with a neural network,”ACM Hypercube Concurrent Computers and Applications, pp. 241-278, Feb. 1988.
Jimenez and Lin, “Dynamic branch prediction with perceptrons,” Technical Report TR2000-08, Dept. of Computer Sciences, The University of Texas, pp. 1-10, 2000.
Jimenez and Lin, “Neural methods for dynamic branch prediction,”ACM Transactions on Computer Systems, 20(4):369-397, 2002.
Jimenez et al., “Boolean formula-based branch prediction for future technologies,”Proceedings of the International Conference on Parallel Architectures and Compilation Techniques, pp. 1-10, Sep. 2001.
Jimenez et al., “The Impact of delay on the design of branch predictors,”Proceedings of the 33rdAnnual International Symposium on Microarchitecture, pp. 1-10, Dec. 2000.
Mulgund et al., “OLIPSA: on-line intelligent processor for situation assessment,”2ndAnnual Symposium and Exhibiton on Situational Awareness in the Tactical Air Environment, pp. 1-13, Jun. 1997.
Reese et al., “Improved splice site detection in genie,”ACM Annual Conference on Research in Computational Molecular Biology, pp. 232-240, 1997.
Schrodt, “Machine Learning,” Chapter 5 in:Patterns Rules and Learning, Version 1.0, 1-76, 1995.

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