Selected publications in 2013

Drug-Domain Interaction Networks in Myocardial Infarction.
Wang H, Zheng H, Azuaje F,Zhao XM.
IEEE Transactions on NanoBioscience (2013)

Based on the integration of several biological resources including two recently published datasets i.e., Drug-target interactions in myocardial infarction (My-DTome) and drug-domain interaction network, this paper reports the association between drugs and protein domains in the context of myocardial infarction (MI).

Human monogenic disease genes have frequently functionally redundant paralogs.
Chen WH, Zhao XM, Noort V and Bork P.
PLoS Computational Biology (2013)

We propose that functional compensation by duplication of genes masks the phenotypic effects of deleterious mutations and reduces the probability of purging the defective genes from the human population; this functional compensation could be further enhanced by higher purification selection between disease genes and their duplicates as well as their orthologous counterpart compared to non-disease genes.

eFG: an electronic resource for Fusarium graminearum.
Liu X, Zhang X, Tang WH, Chen L, Zhao XM.
Database (Oxford) (2013)

In this work, we present a comprehensive database, namely eFG (Electronic resource for Fusarium graminearum), to the community for further understanding this destructive pathogen.

Detecting early-warning signals of type 1 diabetes and its leading biomolecular networks by dynamical network biomarkers
Liu X, Liu R, Zhao XM, Chen L.
BMC Medical Genomics (2013)

In this study, we detected early-warning signals of T1D and its leading biomolecular networks based on serial gene expression profiles of NOD (non-obese diabetic) mice by identifying a new type of biomarker, i.e., dynamical network biomarker (DNB) which forms a specific module for marking the time period just before the drastic deterioration of T1D.

Prediction of S-Glutathionylation Sites Based on Protein Sequences.
Sun C, Shi ZZ, Zhou X, Chen L, Zhao XM.
PLoS ONE (2013)

In this paper, we firstly collect experimentally determined S-glutathionylated proteins and their corresponding modification sites from the literature, and then propose a new method for predicting S-glutathionylation sites by employing machine learning methods based on protein sequence data.

NARROMI: a noise and redundancy reduction technique improves accuracy of gene regulatory network inference.
Zhang X, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, Hao JK, Chen L.
Bioinformatics (2013)

In this work, we present a novel method, namely NARROMI, to improve the accuracy of GRN inference by combining ordinary differential equation-based recursive optimization (RO) and information theory-based mutual information (MI).