Selected publications in 2016

Identifying disease associated miRNAs based on protein domains.
Qin GM, Li RY,Zhao XM.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2016)

In this work, we present a new approach to identify disease associated miRNAs based on domains, the functional and structural blocks of proteins. The results on real datasets demonstrate that our method can effectively identify disease related miRNAs with high precision.

Differential network analysis from cross-platform gene expression data.
Zhang XF, Ou-Yang L, Zhao XM, Yan H.
Scientific Reports (2016)

We introduce a two dimensional joint graphical lasso (TDJGL) model to simultaneously estimate group-specific gene dependency networks from gene expression profiles collected from different platforms and infer differential networks.

Integrative analysis of mutational and transcriptional profiles reveals driver mutations of metastatic breast cancers.
Lee JH, Zhao XM, Yoon I, Lee JY, et al.
Cell Discovery (2016)

We hereby present a novel systems biology approach to identify driver mutations escalating the risk of metastasis based on both exome and RNA sequencing of our collected 78 normal-paired breast cancers.

The exploration of network motifs as potential drug targets from post-translational regulatory networks.
Zhang XD, Song J, Bork P, Zhao XM.
Scientific Reports (2016)

In this work, we construct a post-translational regulatory network (PTRN) consists of phosphorylation and proteolysis processes, which enables us to investigate the regulatory interplays between these two PTMs.

A systematic exploration of the associations between amino acid variants and post-translational modifications.
Qin GM, Hou YB, Zhao XM.
Neurocomputing (2016)

By analyzing the PTM sites and the amino acid mutations, we found that the amino acid mutations co-occurring with PTM sites and PTM cross-talks tend to be deleterious mutations in diseases.

PPIM: A Protein-Protein Interaction Database for Maize.
Zhu G, Wu A, Xu XJ, Xiao PP, Lu L, Zhao XM, et al.
Plant Physiology (2016)

In this work, we present the Protein-Protein Interaction Database for Maize (PPIM), which covers 2,762,560 interactions among 14,000 proteins. The PPIM contains not only accurately predicted PPIs but also those molecular interactions collected from the literature. The database is freely available at http://comp-sysbio.org/ppim with a user-friendly powerful interface.