Elucidating the structure of gene regulatory networks (GRN), i.e., identifying which genes are under control of which transcription factors, is an important challenge to gain insight on a cell's working mechanisms. We present SIRENE, a method to estimate a GRN from a collection of expression data. Contrary to most existing methods for GRN inference, SIRENE requires as input a list of known regulations, in addition to expression data, and implements a supervised machine-learning approach based on learning from positive and unlabeled examples to account for the lack of negative examples. © 2013 Springer Science+Business Media New York.
CITATION STYLE
Mordelet, F., & Vert, J. P. (2013). Supervised inference of gene regulatory networks from positive and unlabeled examples. Methods in Molecular Biology, 939, 47–58. https://doi.org/10.1007/978-1-62703-107-3_5
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