SIGNET: Boolean Rule Determination for Abscisic Acid Signaling

Jerry Jenkins
Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, PMLR 6:215-224, 2010.

Abstract

This paper describes the SIGNET dataset generated for the Causality Challenge. Cellular signaling pathways are most elusive types of networks to access experimentally due to the lack of methods for determining the state of a signaling network in an intact living cell. Boolean network models are currently being used for the modeling of signaling networks due to their compact formulation and ability to adequately represent network dynamics without the need for chemical kinetics. The problem posed in the SIGNET challenge is to determine the set of Boolean rules that describe the interactions of nodes within a plant signaling network, given a set of 300 Boolean pseudodynamic simulations of the true rules. The two solution methods that were presented revealed that the problem can be solved to greater than 99% accuracy.

Cite this Paper

BibTeX
@InProceedings{pmlr-v6-jenkins10a, title = {SIGNET: Boolean Rule Determination for Abscisic Acid Signaling}, author = {Jenkins, Jerry}, booktitle = {Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008}, pages = {215--224}, year = {2010}, editor = {Guyon, Isabelle and Janzing, Dominik and Schölkopf, Bernhard}, volume = {6}, series = {Proceedings of Machine Learning Research}, address = {Whistler, Canada}, month = {12 Dec}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v6/jenkins10a/jenkins10a.pdf}, url = {https://proceedings.mlr.press/v6/jenkins10a.html}, abstract = {This paper describes the SIGNET dataset generated for the Causality Challenge. Cellular signaling pathways are most elusive types of networks to access experimentally due to the lack of methods for determining the state of a signaling network in an intact living cell. Boolean network models are currently being used for the modeling of signaling networks due to their compact formulation and ability to adequately represent network dynamics without the need for chemical kinetics. The problem posed in the SIGNET challenge is to determine the set of Boolean rules that describe the interactions of nodes within a plant signaling network, given a set of 300 Boolean pseudodynamic simulations of the true rules. The two solution methods that were presented revealed that the problem can be solved to greater than 99% accuracy.} }
Endnote
%0 Conference Paper %T SIGNET: Boolean Rule Determination for Abscisic Acid Signaling %A Jerry Jenkins %B Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 %C Proceedings of Machine Learning Research %D 2010 %E Isabelle Guyon %E Dominik Janzing %E Bernhard Schölkopf %F pmlr-v6-jenkins10a %I PMLR %P 215--224 %U https://proceedings.mlr.press/v6/jenkins10a.html %V 6 %X This paper describes the SIGNET dataset generated for the Causality Challenge. Cellular signaling pathways are most elusive types of networks to access experimentally due to the lack of methods for determining the state of a signaling network in an intact living cell. Boolean network models are currently being used for the modeling of signaling networks due to their compact formulation and ability to adequately represent network dynamics without the need for chemical kinetics. The problem posed in the SIGNET challenge is to determine the set of Boolean rules that describe the interactions of nodes within a plant signaling network, given a set of 300 Boolean pseudodynamic simulations of the true rules. The two solution methods that were presented revealed that the problem can be solved to greater than 99% accuracy.
RIS
TY - CPAPER TI - SIGNET: Boolean Rule Determination for Abscisic Acid Signaling AU - Jerry Jenkins BT - Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008 DA - 2010/02/18 ED - Isabelle Guyon ED - Dominik Janzing ED - Bernhard Schölkopf ID - pmlr-v6-jenkins10a PB - PMLR DP - Proceedings of Machine Learning Research VL - 6 SP - 215 EP - 224 L1 - http://proceedings.mlr.press/v6/jenkins10a/jenkins10a.pdf UR - https://proceedings.mlr.press/v6/jenkins10a.html AB - This paper describes the SIGNET dataset generated for the Causality Challenge. Cellular signaling pathways are most elusive types of networks to access experimentally due to the lack of methods for determining the state of a signaling network in an intact living cell. Boolean network models are currently being used for the modeling of signaling networks due to their compact formulation and ability to adequately represent network dynamics without the need for chemical kinetics. The problem posed in the SIGNET challenge is to determine the set of Boolean rules that describe the interactions of nodes within a plant signaling network, given a set of 300 Boolean pseudodynamic simulations of the true rules. The two solution methods that were presented revealed that the problem can be solved to greater than 99% accuracy. ER -
APA
Jenkins, J.. (2010). SIGNET: Boolean Rule Determination for Abscisic Acid Signaling. Proceedings of Workshop on Causality: Objectives and Assessment at NIPS 2008, in Proceedings of Machine Learning Research 6:215-224 Available from https://proceedings.mlr.press/v6/jenkins10a.html.

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