In order to construct complex interaction and regulatory networks, Sun et al. incorporate information from multiple sources, including protein-protein interaction databases, pathway databases, transcription factor target gene predictions and microRNA target gene predictions to generate regulatory networks from an input gene set [6].
Such bow tie structures contain multiple source and target proteins and, in most cases, internal proteins that process the transduced signals.
We show that BowTieBuilder, given multiple source and/or target proteins, infers pathways with satisfactory recall and precision rates and detects the core proteins of each pathway.
Here, we present BowTieBuilder for inferring signal transduction pathways from multiple source and target proteins.
When multiple source and target proteins are provided, we employ a greedy approach, referred to as BowTieBuilder, to construct the signaling pathway P. In the first step, BowTieBuilder initializes the signaling pathway P = (V = S ∩ T, E = ∅, w = ∅) by including the source S and target T nodes, and flagging these nodes as 'not visited'.
But since then, other studies have found that if you get multiple sources of protein throughout the day, that's fine.
The resulting pathways provide the researcher with a interconnected signaling pathway, which unravels the core proteins that integrate and transduce signals from multiple source to multiple target proteins.
By contrast, no difference was found in the concentrations of sEndoglin between samples from non-pregnant women and from early pregnancy controls suggesting that there could be multiple sources of this protein.
Multiple source localization.
