  Constructing gene interaction networks (GINs) from high-throughput gene
expression data is an important and challenging problem in systems biology.
Existing algorithms produce networks that either have undirected and unweighted
edges, or else are constrained to contain no cycles, both of which are
biologically unrealistic. In the present paper we propose a new algorithm,
based on a concept from probability theory known as the phi-mixing coefficient,
that produces networks whose edges are weighted and directed, and are permitted
to contain cycles. Because there is no "ground truth" for genome-wide networks
on a human scale, we analyzed the outcomes of several experiments on lung
cancer, and matched the predictions from the inferred networks with
experimental results. Specifically, we inferred three networks (NSCLC,
Neuro-endocrine NSCLC plus SCLC, and normal) from the gene expression
measurements of 157 lung cancer and 59 normal cell lines, compared with the
outcomes of siRNA screening of 19,000+ genes on 11 NSCLC cell lines, and
analyzed data from a ChIP-Seq experiment to determine putative downstream
targets of the lineage specific oncogenic transcription factor ASCL1. The
inferred networks displayed a scale-free or power law behavior between the
degree of a node and the number of nodes with that degree. There was a strong
correlation between the degree of a gene in the inferred NSCLC network and its
essentiality for the survival of the cells. The inferred downstream
neighborhood genes of ASCL1 in the SCLC network were significantly enriched by
ChIP-Seq determined putative target genes, while no such enrichment was found
in the inferred NSCLC network.
