000224215 001__ 224215
000224215 005__ 20180913064113.0
000224215 037__ $$aREP_WORK
000224215 245__ $$aIdentification of PieceWise Affine Models of Genetic Regulatory Networks: the Data Classification Problem
000224215 269__ $$a2007
000224215 260__ $$c2007
000224215 336__ $$aReports
000224215 520__ $$aIn this paper we consider the identification of PieceWise Affine (PWA) models of Genetic Regulatory Networks (GRNs) and focus on data classification that is a task of the whole identification process. By assuming that gene expression profiles have been split into segments generated by a single affine mode, data classification amounts to group together segments that have been produced by the same mode. In particular, this operation must be performed in a noisy setting and without using any knowledge on the number of modes excited in the experiment. At a mathematical level, classification amounts to find all partitions of the set of segments that verify a statistical criterion and as such it has a combinatorial nature. In order to minimize the computational complexity we propose a pruning strategy for reducing the dimension of the search space. In particular, our approach hinges on a new algorithm for generating in an effcient way all partitions of a finite set that verify a bound on a monotone cost function.
000224215 700__ $$aPorreca, R.
000224215 700__ $$aFerrari-Trecate, G.
000224215 909C0 $$0252594$$pSCI-STI-GFT$$xU13313
000224215 909CO $$ooai:infoscience.tind.io:224215$$pSTI$$preport
000224215 937__ $$aEPFL-REPORT-224215
000224215 970__ $$aPFT07/GFT
000224215 973__ $$aOTHER$$rREVIEWED$$sPUBLISHED
000224215 980__ $$aREPORT