论文标题
通过稀疏优化重建基因调节网络
Reconstruction of gene regulatory network via sparse optimization
论文作者
论文摘要
在本文中，我们根据Dream5基因调节网络推断挑战的公共数据集测试了几种稀疏优化算法。而且我们发现，将20％的监管网络作为先验数据引入已知数据可以为推理算法的参数选择提供基础，从而提高了预测效率和准确性。除了测试常见的稀疏优化方法外，我们还通过包装来开发投票算法。 Dream5数据集中的实验表明，基于稀疏优化的适度关系推断效果很好，比三个数据集中的官方Dream5结果更好地取得了结果。但是，在面对不同数据集的情况下，传统独立算法的性能差异很大，而我们的投票算法在四个数据集中的三个中取得了最佳结果。
In this paper, we tested several sparse optimization algorithms based on the public dataset of the DREAM5 Gene Regulatory Network Inference Challenge. And we find that introducing 20% of the regulatory network as a priori known data can provide a basis for parameter selection of inference algorithms, thus improving prediction efficiency and accuracy. In addition to testing common sparse optimization methods, we also developed voting algorithms by bagging them. Experiments on the DREAM5 dataset show that the sparse optimization-based inference of the moderation relation works well, achieving better results than the official DREAM5 results on three datasets. However, the performance of traditional independent algorithms varies greatly in the face of different datasets, while our voting algorithm achieves the best results on three of the four datasets.
