Mordelet Fantine, Vert Jean-Philippe
Department of Computer Science, Duke University, Durham, NC, USA.
Methods Mol Biol. 2013;939:47-58. doi: 10.1007/978-1-62703-107-3_5.
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.
阐明基因调控网络（GRN）的结构，即确定哪些基因受哪些转录因子的控制，是深入了解细胞工作机制的一项重要挑战。我们提出了SIRENE，一种从表达数据集中估计基因调控网络的方法。与大多数现有的基因调控网络推断方法不同，SIRENE除了需要表达数据作为输入外，还需要一份已知调控的列表，并基于从正例和未标记示例中学习来实现一种监督式机器学习方法，以解决负例缺失的问题。
