Reconstruction of gene regulatory network via sparse optimization
This work addresses gene regulatory network inference for computational biology, but it is incremental as it builds on existing sparse optimization methods with minor enhancements.
The paper tackled gene regulatory network reconstruction by testing and enhancing sparse optimization algorithms on the DREAM5 dataset, showing that incorporating 20% prior knowledge improves efficiency and accuracy, with a voting algorithm achieving top results on three out of four datasets.
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.