Network inference performance complexity: a consequence of topological, experimental and algorithmic determinants.

ABSTRACT: MOTIVATION:Network inference algorithms aim to uncover key regulatory interactions governing cellular decision-making, disease progression and therapeutic interventions. Having an accurate blueprint of this regulation is essential for understanding and controlling cell behavior. However, the utility and impact of these approaches are limited because the ways in which various factors shape inference outcomes remain largely unknown. RESULTS:We identify and systematically evaluate determinants of performance-including network properties, experimental design choices and data processing-by developing new metrics that quantify confidence across algorithms in comparable terms. We conducted a multifactorial analysis that demonstrates how stimulus target, regulatory kinetics, induction and resoluti

SUBMITTER: Muldoon JJ 

PROVIDER: S-EPMC6748731 | biostudies-literature | 2019 Sep

REPOSITORIES: biostudies-literature