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We used a false-discovery rate (FDR) of 0.05, which represents the 95% confidence in the modeled noise distribution, as a cutoff for determining whether the transcriptional intensity of a given transcription unit is higher than the noise.

Recently, it has been demonstrated that the sole analysis of polyA + mRNAs may not give any information on the direct transcriptional output of a given transcription factor/co-activator complex, as cells can buffer global transcription changes by adjusting mRNA decay in parallel ([ 48– 50] and refs therein).

Natural selection would preserve those CRMs that are critical to transcriptional regulation but an organism could tolerate CRMs that had weak affinity for a given transcription factor that did not interfere with transcriptional regulation [59], [60].

It is not clear what factors determine the genome-wide pattern of binding or regulation for a given transcription factor.

We decomposed the TRN into several network motifs, with each network motif potentially associated with a given transcription factor(s).

The percentage of representation of a given transcription factor was compared to its representation in the rest of the microarray.

Multiple probes for a given transcription unit render abundant measurements which allows for further statistical evaluations, and therefore, leads to more accurate results.

We established a robust method that can accurately infer underlying relationships between a given transcription factor and its downstream target genes by integrating different layers of biological data.

Associations between each transcription factor and 39 gene clusters was determined by training the RNN model that mimics the specific network motif for a given transcription factor.

The function of any given transcription factor is determined in large part by its DNA binding specificity, which defines its potential target genes.

Key function of such a probability model is to define the probability that a gene is regulated by a given transcription factor.

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