This paper is in RECOMB, a major bioinformatics conference, though some of the authors are players in ML. So the authors omitted a gentle introduction to gene regulation process.

Shortly speaking, there are ~30000 genes in human, but most of them are "sleeping": their expression level is very low. Gene regulation is a process that enhances or represses the expression level of genes. Since gene (DNA) needs to be transcripted to mRNA to make protein to carry out its function (or to say being turned on), one of main gene regulation mechnism is via transcription. In order to transcript a gene, one or more TFs (transcription factors, a kind of protein) need to bind to particular DNA sequences (called promoters) near the transcription start site. The study of binding between TFs and promoter sequences is a hot topic in biology. People develop both computational algorithms and biological experiments to understand the binding. PSSM is a simple way to model promoters. The localization experiment is a way to measure this binding. And microarray is a way to measure expression level of genes. The goal of this paper is to give a unified model for PSSM, localization, and microarray expression.

I think the paper presented a serious attempt to model via Bayesian Networks. However, I feel the paper delves into details too early and for too long. I guess the author want to make this work reproduceable. It would be more understandable if the author provides a gentle introduction to Bayesina Networks and some rationale to develop such kind of network structure. It would required a more careful organization of the limited space.