000130978 001__ 130978
000130978 005__ 20180317095045.0
000130978 02470 $$2ISI$$a000262404502352
000130978 037__ $$aCONF
000130978 245__ $$aImplicit Methods for Probabilistic Modeling of Gene Reglatory Networks
000130978 269__ $$a2008
000130978 260__ $$c2008
000130978 336__ $$aConference Papers
000130978 520__ $$aIn silico modeling of Gene Regulatory Networks (GRN) has recently aroused a lot of interest in the biological community for modeling and understanding complex pathways. Boolean Networks (BN) are a common modeling tool for in silico dynamic analysis of such pathways. Although they are known to have effectively modeled many real and complex regulatory networks, they are deterministic in nature and have shortcomings in modeling non-determinism that is inherent in biological systems. Probabilistic Boolean Networks (PBN) have been proposed to counter these shortcomings. The capabilities of PBNs have been so far under-utilised because of the lack of an efficient PBN toolbox. This work addresses some issues associated with traditional methods of PBN representation and proposes efficient algorithms to model gene regulatory networks using PBNs.
000130978 700__ $$0240151$$aGarg, Abhishek$$g165204
000130978 700__ $$0(EPFLAUTH)171613$$aBanerjee, Debasree$$g171613
000130978 700__ $$0240269$$aDe Micheli, Giovanni$$g167918
000130978 7112_ $$aIEEE EMBC 2008: 30th Annual Conference of the EMB Society$$cVancouver, Canada$$dAugust 20-24, 2008
000130978 773__ $$tProceedings of IEEE EMBC 2008: 30th Annual Conference of the EMB Society
000130978 8564_ $$s422132$$uhttps://infoscience.epfl.ch/record/130978/files/embc08.pdf$$yn/a$$zn/a
000130978 909CO $$ooai:infoscience.tind.io:130978$$pIC$$pconf$$pSTI
000130978 909C0 $$0252283$$pLSI1$$xU11140
000130978 917Z8 $$x112915
000130978 917Z8 $$x112915
000130978 937__ $$aEPFL-CONF-130978
000130978 973__ $$aEPFL$$rREVIEWED$$sPUBLISHED
000130978 980__ $$aCONF