ABSTRACT
Elucidating the molecular mechanisms that govern plant growth has been an important topic in plant research, and current advances in large scale data generation call for computational tools that efficiently combine these different data sources to generate novel hypotheses. In this work, we present a novel, integrated network that combines multiple large scale data sources to characterize growth regulatory genes in Arabidopsis, one of the main plant model organisms. The contributions of this work are two-fold: first we characterize a set of carefully selected growth regulators with respect to their connectivity patterns in the integrated network, and subsequently we explore to which extent these connectivity patterns can be used to suggest new growth regulators. Using a large-scale comparative study, we design new supervised machine learning methods to prioritize growth regulators. Our results show that these methods significantly improve upon current state-of-the-art prioritization techniques, and are able to suggest meaningful new growth regulators. In addition, the integrated network is made available to the scientific community, providing a rich data source that will be useful for many biological processes, not necessarily restricted to plant growth.
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