It's the machine that matters: Predicting gene function and phenotype from protein networks

Abstract

Increasing knowledge about the organization of proteins into complexes, systems, and pathways has led to a flowering of theoretical approaches for exploiting this knowledge in order to better learn the functions of proteins and their roles underlying phenotypic traits and diseases. Much of this body of theory has been developed and tested in model organisms, relying on their relative simplicity and genetic and biochemical tractability to accelerate the research. In this review, we discuss several of the major approaches for computationally integrating proteomics and genomics observations into integrated protein networks, then applying guilt-by-association in these networks in order to identify genes underlying traits. Recent trends in this field include a rising appreciation of the modular network organization of proteins underlying traits or mutational phenotypes, and how to exploit such protein modularity using computational approaches related to the internet search algorithm PageRank. Many protein network-based predictions have recently been experimentally confirmed in yeast, worms, plants, and mice, and several successful approaches in model organisms have been directly translated to analyze human disease, with notable recent applications to glioma and breast cancer prognosis.

Genetic dissection of the biotic stress response using a genome-scale gene network for rice

“…In this "guiltby-association" approach, we prioritized candidate genes for each biological process based on network connections to known genes in those processes, assessing predictive accuracy by using crossvalidation and receiver operating characteristic (ROC) analysis. Our previous study demonstrated that superior prediction performance can be achieved by using methods that consider not only direct network neighbors but also indirect ones (34). Therefore, we prioritized candidate genes by using both direct and indirect network neighbors via Gaussian smoothing (17).…”

Network-assisted approaches for human disease research

“…There are two conceptually distinct strategies for network propagation (Wang & Marcotte 2010). In the direct neighborhood strategy, prior disease information propagates only to direct neighbors.…”

Network-Based Interpretation of Genomic Variation Data

“…These labels are propagated to the neighbors of those genes. In order to determine the best method for label propagation, they considered six different methods: (1) neighbor counting [44], (2) naïve Bayes label propagation [45], (3) iterative ranking [46,47], (4) Gaussian field label propagation [48], (5) Markov clustering based on simulation of stochastic flow [47], and (6) a model based on electronic circuits [49]. Their experiments reveal that the naïve Bayes label propagation approach best suits their purpose.…”