Inference of Molecular Regulatory Systems Using Statistical Path-Consistency Algorithm.

ABSTRACT: One of the key challenges in systems biology and molecular sciences is how to infer regulatory relationships between genes and proteins using high-throughout omics datasets. Although a wide range of methods have been designed to reverse engineer the regulatory networks, recent studies show that the inferred network may depend on the variable order in the dataset. In this work, we develop a new algorithm, called the statistical path-consistency algorithm (SPCA), to solve the problem of the dependence of variable order. This method generates a number of different variable orders using random samples, and then infers a network by using the path-consistent algorithm based on each variable order. We propose measures to determine the edge weights using the corresponding edge weights in the infer

SUBMITTER: Yan Y 

PROVIDER: S-EPMC9142129 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature