title: Finding fractal patterns in molecular interaction networks: a case study in Alzheimer's disease.
abstract: The identification of molecular entities involved in human diseases has been a primary focus of post-genomic biomedicine for pursuing the clinical goals of diagnosis and therapeutic treatment. An emerging perspective in systems biology is that the essential biological roles of molecular entities seem to be well correlated with general molecular network properties. Several types of biological complex networks, including protein interaction networks, have a feature of scale-free networks that relates to fractals (multi-scale self-similarity). Using Alzheimer's Disease (AD) as a case study, we constructed an AD-relevant protein interaction subnetwork. We further developed a computational framework based on Ant Colony Optimisation (ACO) to rank disease network relevant nodes. In this framework, the task of ranking nodes is represented as the problem of finding optimal density distributions of 'ant colonies' on all nodes of the network. Our results also revealed fractal-like properties of the network.
authors.affiliation: School of Informatics, Indiana University, Indianapolis, IN 46202, USA. wu@iupui.edu
authors.forename: Xiaogang
authors.initials: X
authors.lastname: Wu
authors.forename: Tianxiao
authors.initials: T
authors.lastname: Huan
authors.forename: Ragini
authors.initials: R
authors.lastname: Pandey
authors.forename: Tianshou
authors.initials: T
authors.lastname: Zhou
authors.forename: Jake Y
authors.initials: JY
authors.lastname: Chen
mesh_terms: D000544:Alzheimer Disease / Q000378:metabolism*; D006801:Humans; D011506:Proteins / Q000737:chemistry*; D049490:Systems Biology / Q000379:methods*
chemical_list: D011506:Proteins