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On the Difficulty of Inferring Gene Regulatory Networks: A Study of the Fitness Landscape Generated by Relative Squared Error
[chapter]
2010
Lecture Notes in Computer Science
Inferring gene regulatory networks from expression profiles is a challenging problem that has been tackled using many different approaches. When posed as an optimization problem, the typical goal is to minimize the value of an error measure, such as the relative squared error, between the real profiles and those generated with a model whose parameters are to be optimized. In this paper, we use recurrent neural networks to model regulatory interactions and study systematically the "fitness
doi:10.1007/978-3-642-14156-0_7
fatcat:u4lb64urinao7j6y43v5axjnu4