采用PageRank算法探测生物过程中的临界点

Identifying Critical Transition with PageRank Algorithm in a Biological Process

摘要: 生物高通量表达数据包含了海量信息，因此在以之为基础的研究中所面对的问题，可以视作一种信息提取问题。受此驱动，我们对互联网领域中久负盛名的PageRank算法进行改造，并将其与动态网络标志物理论相结合，来探测生物过程中的临界点。我们的算法通过了随机生成的模拟数据的检验，并在实验数据中得到与文献中已发表方法一致的结果。

Abstract: With the belief that high-throughput datasets hold all the necessary information we want, a prob-lem of information retrieval confronts us. As PageRank algorithm achieves a great success in dealing with such a problem in the field of Internet, we adapt it for high-throughput datasets in combination with the theory of dynamical network biomarker, and try to identify a critical transi-tion in the biological processes. Our adapted PageRank algorithm successfully identifies the des-ignated critical points in data simulations and it also produces the same results with the earlier works when applied to experimental datasets.