The single-sample network module biomarkers(sNMB)method reveals the pre-deterioration stage of disease progression

The progression of complex diseases generally involves a pre-deterioration stage that occurs during the transition from a healthy state to disease deterioration,at which a drastic and qualitative shift occurs.The development of an effective approach is urgently needed to identify such a pre-deterioration stage or critical state just before disease deterioration,which allows the timely implementation of appropriate measures to prevent a catastrophic transition.However,identifying the pre-deterioration stage is a challenging task in clinical medicine,especially when only a single sample is available for most patients,which is responsible for the failure of most statistical methods.In this study,a novel computational method,called single-sample network module biomarkers(sNMB),is presented to predict the pre-deterioration stage or critical point using only a single sample.Specifically,the proposed single-sample index effectively quantifies the disturbance caused by a single sample against a group of given reference samples.Our method successfully detected the early warning signal of the critical transitions when applied to both a numerical simulation and four real datasets,including acute lung injury,stomach adenocarcinoma,esophageal carcinoma,and rectum adenocarcinoma.In addition,it provides signaling biomarkers for further practical application,which helps to discover prognostic indicators and reveal the underlying molecular mechanisms of disease progression.

Recovering context-specific gene network modules from expression data: A brief review

With the popularization of microarray experi-ments in biomedical laboratories,how to make context-specific knowledge discovery from expression data becomes a hot topic.While the static“reference networks”for key model organisms are nearly at hand,the endeavors to recover context-specific network modules are still at the beginning.Currently,this is achieved through filtering existing edges of the ensemble reference network or constructing gene networks ab initio.In this paper,we briefly review recent progress in the field and point out some research directions awaiting improved work,includ-ing expression-data-guided revision of reference networks.

FunMod： A Cytoscape Functional Modules in Networks Plugin for Identifying Undirected Protein-Protein

The characterization of the interacting behaviors of complex biological systems is a pri- mary objective in protein protein network analysis and computational biology. In this paper we present FunMod, an innovative Cytoscape version 2.8 plugin that is able to mine undirected pro- rein-protein networks and to infer sub-networks of interacting proteins intimately correlated with relevant biological pathways. This plugin may enable the discovery of new pathways involved in dis- eases. In order to describe the role of each protein within the relevant biological pathways, FunMod computes and scores three topological features of the identified sub-networks. By integrating the results from biological pathway clustering and topological network analysis, FunMod proved to be useful for the data interpretation and the generation of new hypotheses in two case studies.

Co-regulated Protein Functional Modules with Varying Activities in Dynamic PPI Networks

Current methods for the detection of differential gene expression focus on finding individual genes that may be responsible for certain diseases or external irritants. However, for common genetic diseases, multiple genes and their interactions should be understood and treated together during the exploration of disease causes and possible drug design. The present study focuses on analyzing the dynamic patterns of co-regulated modules during biological progression and determining those having remarkably varying activities, using the yeast cell cycle as a case study. We first constructed dynamic active protein-protein interaction networks by modeling the activity of proteins and assembling the dynamic co-regulation protein network at each time point. The dynamic active modules were detected using a method based on the Bayesian graphical model and then the modules with the most varied dispersion of clustering coefficients, which could be responsible for the dynamic mechanism of the cell cycle, were identified. Comparison of results from our functional module detection with the state-of-art functional module detection methods and validation of the ranking of activities of functional modules using GO annotations demonstrate the efficacy of our method for narrowing the scope of possible essential responding modules that could provide multiple targets for biologists to further experimentally validate.