Gene regulatory network inference is one of the central problems in computational biology. We need models that integrate the variety of data available in order to use their complementarity information to overcome the issues of noisy and limited data. BENIN: Biologically Enhanced Network INference is our proposal to integrate data and infer more accurate networks. BENIN is a general framework that jointly considers different types of prior knowledge with expression datasets to improve the network inference. The method states the network inference as a feature selection problem and uses a popular penalized regression method, the Elastic net, combined with bootstrap resampling to solve it. BENIN significantly outperforms the state-of-the-art methods on the simulated data from the DREAM 4 challenge when combining genome-wide location data, knockout gene expression data, and time series expression data.

中文翻译：

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贝宁：生物增强网络推理

基因调控网络推断是计算生物学的核心问题之一。我们需要整合各种可用数据的模型，以便利用它们的互补性信息来克服嘈杂和有限数据的问题。BENIN：生物增强网络推断是我们整合数据和推断更准确网络的建议。BENIN 是一个通用框架，它将不同类型的先验知识与表达数据集共同考虑以改进网络推理。该方法将网络推理描述为一个特征选择问题，并使用一种流行的惩罚回归方法，即弹性网络，结合自举重采样来解决它。在结合全基因组位置数据时，BENIN 在 DREAM 4 挑战的模拟数据上明显优于最先进的方法，