Regulators Page (Inferelator Network)

Regulators page lists regulatory influences identified through Inferelator algorithm.

The Inferelator is an algorithm for infering predictive regulatory networks from gene expression data. It does so by selecting the regulators (transcription factors or environmental factors) whose levels are most predictive of each gene or bicluster's expression.

Columns

• Regulator Locus: Locus tag of the TF and its product linked to gene page
• Interaction: Direction of the regulatory influence identified. up-regulates vs down-regulates
• Target Bicluster: Bicluster predicted to be regulated by given TF. Link will take you to the module page.
• Weight: Weight of the regulatory influence. It can be positive (up-regulates) or negative (down-regulates). Higher absolute value of the weight is associated with confidence of the influence prediction.
• Regulator Taxonomy: List all other modules that are predicted to be regulated by the same TF.

Filters: Regulators table can be further filtered by searching for a specific Regulator, Interaction or weight.

Reference: Bonneau R, Reiss DJ, Shannon P, Facciotti M, Hood L, Baliga NS, Thorsson V. The Inferelator: an algorithm for learning parsimonious regulatory networks from systems-biology data sets de novo. Genome Biol. 2006;7(5):R36