FSM: A Fast Similarity Measurement for Gene Regulatory Networks via Genes’
Influence Power
Abstract
The problem of graph similarity measurement
is fundamental in both complex networks and
bioinformatics researches. Gene regulatory networks (GRNs) describe the interactions between
the molecules in organisms, and are widely studied in the fields of medical AI. By measuring the
similarity between GRNs, significant information
can be obtained to assist the applications like gene
functions prediction, drug development and medical diagnosis. Most of the existing similarity measurements have been focusing on the graph isomorphisms and are usually NP-hard problems. Thus,
they are not suitable for applications in biology and
clinical research due to the complexity and largescale features of real-world GRNs. In this paper,
a fast similarity measurement method called FSM
for GRNs is proposed. Unlike the conventional
measurements, it pays more attention to the differences between those influential genes. For the
convenience and reliability, a new index defined as
influence power is adopted to describe the influential genes which have greater position in a GRN.
FSM was applied in nine datasets of various scales
and is compared with state-of-art methods. The
results demonstrated that it ran significantly faster
than other methods without sacrificing measurement performance