  Motivation: Identifying interaction clusters of large gene regulatory
networks (GRNs) is critical for its further investigation, while this task is
very challenging, attributed to data noise in experiment data, large scale of
GRNs, and inconsistency between gene expression profiles and function modules,
etc. It is promising to semi-supervise this process by prior information, but
shortage of prior information sometimes make it very challenging. Meanwhile, it
is also annoying, and sometimes impossible to discovery gold standard for
evaluation of clustering results.\\ Results: With assistance of an online
enrichment tool, this research proposes a semi-supervised hierarchical
clustering method via deconvolved correlation matrix~(SHC-DC) to discover
interaction clusters of large-scale GRNs. Three benchmark networks including a
\emph{Ecoli} network and two \emph{Yeast} networks are employed to test
semi-supervision scheme of the proposed method. Then, SHC-DC is utilized to
cluster genes in sleep study. Results demonstrates it can find interaction
modules that are generally enriched in various signal pathways. Besides the
significant influence on blood level of interleukins, impact of sleep on
important pathways mediated by them is also validated by the discovered
interaction modules.
