Freschi Valerio
DiSBeF-Department of Base Sciences and Fundamentals, University of Urbino, Urbino, Italy.
J Comput Biol. 2011 Aug;18(8):987-96. doi: 10.1089/cmb.2010.0232. Epub 2011 Jun 24.
Biological networks reconstruction is a crucial step towards the functional characterization and elucidation of living cells. Computational methods for inferring the structure of these networks are of paramount importance since they provide valuable information regarding organization and behavior of the cell at a system level and also enable careful design of wet-lab experiments. Despite many recent advances, according to the scientific literature, there is room for improvements from both the efficiency and the accuracy point of view in link prediction algorithms. In this article, we propose a new method for the inference of biological networks that makes use of a notion of similarity between graph vertices within the framework of graph regularization for ranking the links to be predicted. The proposed approach results in more accurate classification rates in a wide range of experiments, while the computational complexity is reduced by two orders of magnitude with respect to many current state-of-the-art algorithms.
生物网络重建是朝着活细胞功能表征和阐明迈出的关键一步。用于推断这些网络结构的计算方法至关重要，因为它们在系统层面提供了有关细胞组织和行为的有价值信息，还能精心设计湿实验室实验。尽管最近有许多进展，但根据科学文献，从链路预测算法的效率和准确性角度来看仍有改进空间。在本文中，我们提出了一种用于生物网络推断的新方法，该方法在图正则化框架内利用图顶点之间的相似性概念对要预测的链路进行排序。在广泛的实验中，所提出的方法能得到更准确的分类率，同时相对于许多当前的先进算法，计算复杂度降低了两个数量级。
