Selected publications in 2018

DrPOCS: Drug repositioning based on projection onto convex sets.
Wang YY, Cui CF, Qi LY, Yan H, Zhao XM.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2018)

In this paper, by formulating the drug-disease associations as a low-rank matrix, we propose a novel method, namely DrPOCS, to identify candidate indications of old drugs based on projection onto convex sets (POCS).

MVP: a microbe-phage interaction database.
Gao NL, Zhang C, Zhang Z, Hu S, Lercher MJ, Zhao XM, Bork P, Liu Z, Chen WH.
Nucleic Acids Research (2018)

The main purpose of MVP (Microbe Versus Phage) is to provide a comprehensive catalog of phage–microbe interactions and assist users to select phage(s) that can target (and potentially to manipulate) specific microbes of interest.

Selected publications in 2017

HISP: A Hybrid Intelligent Approach for Identifying Directed Signaling Pathways.
Zhao XM, Li S.
Journal of Molecular Cell Biology (2017)

In this paper, we propose a novel hybrid intelligent method, namely HISP (Hybrid Intelligent approach for identifying directed Signaling Pathways), to determine both the topologies of signaling pathways and the direction of signaling flows within a pathway based on integer linear programming and genetic algorithm. By integrating the protein−protein interaction, gene expression, and gene knockout data, our HISP approach is able to determine the optimal topologies of signaling pathways in an accurate way.

Predicting new indications of compounds with a network pharmacology approach: Liuwei Dihuang Wan as a case study.
Wang YY, Bai H, Zhang RZ, Yan H, Ning K, Zhao XM.
Oncotarget (2017)

In this paper, we introduce a new network pharmacology approach, namely PINA, to predict potential novel indications of old drugs based on the molecular networks affected by drugs and associated with diseases.

A CPU/MIC Collaborated Parallel Framework for GROMACS on Tianhe-2 Supercomputer.
Gao NL, Zhang C, Zhang Z, Hu S, Lercher MJ, Zhao XM, Bork P, Liu Z, Chen WH.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

In this paper, we propose a CPU and Intel® Xeon Phi Many Integrated Core (MIC) collaborated parallel framework to accelerate GROMACS using the offload mode on a MIC coprocessor, with which the performance of GROMACS is improved significantly, especially with the utility of Tianhe-2 supercomputer. Furthermore, we optimize GROMACS so that it can run on both the CPU and MIC at the same time. In addition, we accelerate multi-node GROMACS so that it can be used in practice.

EmDL: Extracting miRNA-Drug Interactions from Literature.
Xie WB, Yan H, Zhao XM.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

In this paper, we present a novel text mining approach, named as EmDL (Extracting miRNA-Drug interactions from Literature), to extract the relationships of miRNAs affecting drug efficacy from literature.

PCID: A Novel Approach for Predicting Disease Comorbidity by Integrating Multi-scale Data.
He F, Zhu G, Wang YY, Zhao XM.
IEEE/ACM Transactions on Computational Biology and Bioinformatics (2017)

By investigating the factors underlying disease comorbidity, e.g., mutated genes and rewired protein-protein interactions (PPIs), we here present a novel algorithm to predict disease comorbidity by integrating multi-scale data ranging from genes to phenotypes.

CSTEA: a webserver for the Cell State Transition Expression Atlas.
Zhu G, Yang H, Chen X, Wu J, Zhang Y, Zhao XM.
Nucleic Acids Research (2017)

Here, we present CSTEA (Cell State Transition Expression Atlas), a webserver that organizes, analyzes and visualizes the time-course gene expression data during cell differentiation, cellular reprogramming and trans-differentiation in human and mouse.

PhosD: inferring kinase-substrate interactions based on protein domains.
Qin GM, Li RY, Zhao XM.
Bioinformatics (2017)

In this paper, we propose a novel probabilistic model named as PhosD to predict kinase–substrate relationships based on protein domains with the assumption that kinase–substrate interactions are accomplished with kinase–domain interactions.

GEAR: A database of Genomic Elements Associated with drug Resistance.
Wang YY, Chen WH, Xiao PP, Xie WB, Luo Q, Bork P, Zhao XM.
Scientific Reports (2017)

Here, we present GEAR (A database of Genomic Elements Associated with drug Resistance) that aims to provide comprehensive information about genomic elements (including genes, single-nucleotide polymorphisms and microRNAs) that are responsible for drug resistance.

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.

Selected publications in 2015

Oxidized glutathione (GSSG) inhibits epithelial sodium channel activity in primary alveolar epithelial cells.
Downs CA, Kreiner L,Zhao XM, Trac P, Johnson NM, et al.
American Journal of Physiology-Lung Cellular and Molecular Physiology (2015)

In the present study, we used single channel patch-clamp recordings to examine the effect of oxidative stress, via direct application of glutathione disulfide (GSSG), on ENaC activity.

Identifying cancer-related microRNAs based on gene expression data.
Zhao XM, Liu KQ, Zhu G, He F, Duval B, Richer JM, Huang DS, Jiang CJ, Hao JK, Chen L.
Bioinformatics (2015)

We present a novel computational framework to identify the cancer-related miRNAs based solely on gene expression profiles without requiring either miRNA expression data or the matched gene and miRNA expression data.

Conditional mutual inclusive information enables accurate quantification of associations in gene regulatory networks
Zhang X, Zhao J, Hao JK, Zhao XM, Chen L.
Nucleic Acids Research (2015)

In this work, to overcome the problems, we propose a novel concept, namely conditional mutual inclusive information (CMI2), to describe the regulations between genes. Furthermore, with CMI2, we develop a new approach, namely CMI2NI (CMI2-based network inference), for reverse-engineering GRNs.

jNMFMA: a joint non-negative matrix factorization meta-analysis of transcriptomics data.
Wang HQ, Zheng CH, Zhao XM.
Bioinformatics (2015)

This article proposes a new meta-analysis method for identification of DEGs based on joint non-negative matrix factorization (jNMFMA).

Selected publications in 2014

Cascleave 2.0, a new approach for predicting caspase and granzyme cleavage targets.
Wang M,Zhao XM, Tan H, Akutsu T, Whisstock J, and Song J.
Bioinformatics (2014)

We develop a new bioinformatics tool, termed Cascleave 2.0, which builds on previous success of the Cascleave tool for predicting generic caspase cleavage sites.

Comments on "Human Dominant Disease Genes Are Enriched in Paralogs Originating from Whole Genome Duplication".
Chen WH, Zhao XM, Noort V and Bork P.
PLoS Computational Biology (2014)

This Formal Comment is a response to Singh et al., “Human Dominant Disease Genes are Enriched in Paralogs Originating from Whole Genome Duplication,” by the authors of the original research article “Human Monogenic Disease Genes Have Frequently Functionally Redundant Paralogs.”

Network-based biomarkers for complex diseases.
Zhao XM, Chen L.
Journal of Theoretical Biology (2014)

In this special issue, we report the recent progress on computational approaches that are developed to identify biomarkers for complex diseases based on biological networks.

Pattern recognition in bioinformatics
Zhao XM, Ngom A, Hao JK.
Neurocomputing (2014)

Pattern recognition has been proven useful for handling and interpreting the accumulating large amount of biological data, and is widely used in bioinformatics.

A survey on computational approaches to identifying disease biomarkers based on molecular networks.
Qin G, Zhao XM.
Journal of theoretical biology (2014)

In this paper, we surveyed the recent progress on the computational approaches that have been developed to identify disease biomarkers based on molecular networks.

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).

Selected publications in 2012

A systems biology approach to identifying the signaling network regulated by Rho-GDI-γ during neural stem cell differentiation.
Wang J, Hu F, Cheng H, Zhao XM, Wen T.
Molecular BioSystems (2012)

Therefore, a novel systems biology approach is presented here to identify putative signalling pathways regulated by Rho-GDI-γ during NSC differentiation, and these pathways can provide insights into the NSC differentiation mechanisms.

FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model.
Wang M, Zhao XM, Takemoto K, Xu H, Li Y, Akutsu T, Song J.
PLoS ONE (2012)

We built a two-stage random forest (RF) model, termed as FunSAV, to predict the functional effect of SAVs by combining sequence, structure and residue-contact network features with other additional features that were not explored in previous studies.

Identifying dysregulated pathways in cancers from pathway interaction networks.
Liu KQ, Liu ZP, Hao JK, Chen L, Zhao XM.
BMC Bioinformatics (2012)

In this paper, we propose a novel approach to identify dysregulated pathways in cancer based on a pathway interaction network.

Identifying disease genes and module biomarkers by differential interactions.
Liu X, Liu ZP, Zhao XM, Chen L.
Journal of the American Medical Informatics Association : JAMIA (2012)

In this paper, we present a novel approach to predict disease genes and identify dysfunctional networks or modules, based on the analysis of differential interactions between disease and control samples, in contrast to the analysis of differential gene or protein expressions widely adopted in existing methods.

Predicting drug targets based on protein domains.
Wang YY, Nacher JC, Zhao XM.
Molecular BioSystems (2012)

Here, we present a novel statistical approach, namely PDTD (Predicting Drug Targets with Domains), to predict potential target proteins of new drugs based on derived interactions between drugs and protein domains.

Exploring drug combinations in genetic interaction network.
Wang YY, Xu KJ, Song J, Zhao XM.
BMC Bioinformatics (2012)

In this work, we present a network biology approach to investigate drug combinations and their target proteins in the context of genetic interaction networks and the related human pathways, in order to better understand the underlying rules of effective drug combinations.

Inferring gene regulatory networks from gene expression data by path consistency algorithm based on conditional mutual information.
Zhang X, Liu K, Liu ZP, Duval B, Richer JM, Zhao XM, Hao JK, Chen L.
Bioinformatics (2012)

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).

Selected publications in 2011

Prediction of drug combinations by integrating molecular and pharmacological data.
Zhao XM, Iskar M, Zeller G, Kuhn M, van Noort V, Bork P.
PLOS Computational Biology (2011)

Here, we present a novel computational approach to predict drug combinations by integrating molecular and pharmacological data.

Drug discovery in the age of systems biology: the rise of computational approaches for data integration.
Iskar M, Zeller G, Zhao XM, van Noort V, Bork P.
Current Opinion in Biotechnology (2011)

We discuss here how computational data integration enables systemic views on a drug's action and allows to tackle complex problems such as the large-scale prediction of drug targets, drug repurposing, the molecular mechanisms, cellular responses or side effects.

DIPOS: database of interacting proteins in Oryza sativa.
Sapkota A, Liu X, Zhao XM, Cao Y, Liu J, Liu ZP, Chen L.
Molecular BioSystems (2011)

The database of interacting proteins in Oryza sativa (DIPOS) provides comprehensive information of interacting proteins in rice, where the interactions are predicted using two computational methods, i.e., interologs and domain based methods.

Selected publications in 2010

Global Gene Profiling of Laser-Captured Pollen Mother Cells Indicates Molecular Pathways and Gene Subfamilies Involved in Rice Male Meiosis.
Tang X, Zhang ZY, Zhang WJ, Zhao XM, Li X, Zhang D, Liu QQ, Tang WH.
Plant Physiology (2010)

We used laser-capture microdissection of rice (subsp. japonica) stamens to isolate PMCs and their transcripts, followed by transcriptome analysis using microarray hybridization. Using two-color probe hybridization with Agilent 60-mer oligomicroarrays, PMC transcripts were compared with transcripts from two tissues, tricellular pollen (TCP), which comprises three nondividing cells, and seedling, which contains many mitotic dividing cells.

A network approach to predict pathogenic genes for Fusarium graminearum.
Liu X, Tang WH, Zhao XM, Chen L.
PLoS ONE (2010)

In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data.

APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility.
Xia JF, Zhao XM, Song J, Huang DS.
BMC Bioinformatics (2010)

In this work, we introduce an efficient approach that uses support vector machine (SVM) to predict hot spot residues in protein interfaces.

A Systems biology approach to identify effective cocktail drugs.
Wu Z, Zhao XM, Chen L.
BMC Systems Biology (OSB2009 special issue) (2010)

In this paper, we presented a novel network-based systems biology approach to identify effective drug combinations by exploiting high throughput data.

FGsub: Fusarium graminearum protein subcellular localization prediction from primary structures.
Sun C, Zhao XM, Tang W, Chen L.
BMC Systems Biology (OSB2009 special issue) (2010)

In this paper, we developed a novel predictor, namely FGsub, to predict F. graminearum protein subcellular localizations from the primary structures.

Analysis of Gene Expression Data Using RPEM Algorithm in Normal Mixture Model with Dynamic Adjustment of Learning Rate.
Zhao XM, Cheung YM, Huang DS.
International Journal of Pattern Recognition and Artificial Intelligence (2010)

In this paper, we therefore apply a one-step approach, namely Rival Penalized Expectation-Maximization (RPEM) algorithm, to analyze the gene expression data.

A discriminative approach to identifying domain-domain interactions from protein-protein interactions.
Zhao XM, Chen L, Aihara K.
Proteins (2010)

In this article, we propose a novel discriminative approach for predicting DDIs based on both protein–protein interactions (PPIs) and the derived information of non-PPIs.

Selected publications in 2009 and 2008

FPPI: Fusarium graminearum protein-protein interaction database.
Zhao XMZhang XW, Tang WH, Chen L.
Journal of Proteome Research (2009)

F. graminearum protein−protein interaction (FPPI) database provides comprehensive information of protein−protein interactions (PPIs) of F. graminearum predicted based on both interologs from several PPI databases of seven species and domain−domain interactions experimentally determined based on protein structures.

A network approach to predict pathogenic genes for Fusarium graminearum.
Liu X, Tang WH, Zhao XM, Chen L.
PLoS ONE (2009)

In this paper, a novel systems biology approach is presented to predict pathogenic genes of F. graminearum based on molecular interaction network and gene expression data.

Uncovering signal transduction networks from high-throughput data by integer linear programming.
Zhao XM, Wang RS, Chen L, Aihara K.
Nucleic Acids Research (2008)

In this article, we propose a novel method for uncovering signal transduction networks (STNs) by integrating protein interaction with gene expression data.

Gene function prediction using labeled and unlabeled data.
Zhao XM, Wang Y, Chen L, Aihara K.
BMC Bioinformatics (2008)

In this paper, we present a new technique, namely Annotating Genes with Positive Samples (AGPS), for defining negative samples in gene function prediction.

Protein classification with imbalanced data.
Zhao XM, Li X, Chen L, Aihara K.
Proteins (2008)

This article presents a new technique for protein classification with imbalanced data.

Protein domain annotation with integration of heterogeneous information sources.
Zhao XM, Wang Y, Chen L, Aihara K.
Proteins (2008)

In this article, two new methods, that is, the threshold-based classification method and the support vector machines method, are proposed for protein domain function prediction by integrating heterogeneous information sources, including protein–domain mapping features, domain–domain interactions, and domain coexisting features.

A network approach to predict pathogenic genes for Fusarium graminearum.
Zhao XM, Chen L, Aihara K.
Amino Acids (2008)

In this review, we provide a comprehensive description of the computational methods that are currently applicable to protein function prediction, especially from the perspective of machine learning.

Selected publications in 2007-before

A new technique for selecting features from protein sequences.
Zhao XMDu JX, Wang HQ, Zhu Y, Li Y.
International Journal of Pattern Recognition and Artificial Intelligence (2006)

A new method for selecting features from protein sequences is proposed in this paper.

Classifying protein sequences using hydropathy blocks.
Huang DS, Zhao XM, Huang GB, Cheung YM.
Pattern Recognition (2006)

This paper presents a new method for classifying protein sequences based upon the hydropathy blocks occurring in protein sequences.

A novel approach to extracting features from motif content and protein composition for protein sequence classification.
Zhao XM, Cheung YM, Huang DS.
Neural Networks (2005)

This paper presents a novel approach to extracting features from motif content and protein composition for protein sequence classification.