Selected publications in 2020

OGEE v3: Online GEne Essentiality database with increased coverage of organisms and human cell lines.
Gurumayum S, Jiang P, Hao X, Campos TL, Young ND, Korhonen PK, Gasser RB, Bork P, Zhao XM, He LJ, Chen WH.
Nucleic Acids Res. (2020)

OGEE v3 contains gene essentiality datasets for 91 species; almost doubled from 48 species in previous version. To accommodate recent advances on human cancer essential genes (as known as tumor dependency genes) that could serve as targets for cancer treatment and/or drug development, we expanded the collection of human essential genes from 16 cell lines in previous to 581.

mMGE: a database for human metagenomic extrachromosomal mobile genetic elements.
Lai S, Jia L, Subramanian B, Pan S, Zhang J, Dong Y, Chen WH, Zhao XM.
Nucleic Acids Res. (2020)

Here we present mMGE, a comprehensive catalog of 517 251 non-redundant eMGEs, including 92 492 plasmids and 424 759 phages, derived from diverse body sites of 66 425 human metagenomic samples.

STAB: a spatio-temporal cell atlas of the human brain.
Song L, Pan S, Zhang Z, Jia L, Chen WH, Zhao XM.
Nucleic Acids Res. (2020)

Here, we present STAB (a Spatio-Temporal cell Atlas of the human Brain), a database consists of single-cell transcriptomes across multiple brain regions and developmental periods. Right now, STAB contains single-cell gene expression profiling of 42 cell subtypes across 20 brain regions and 11 developmental periods.

Docking sites inside Cas9 for adenine base editing diversification and RNA off-target elimination.
Li S, Yuan B, Cao J, Chen J, Chen J, Qiu J, Zhao XM, Wang X, Qiu Z, Cheng TL..
Nature communications. (2020)

Here, functional ABE variants with diversified editing scopes and reduced RNA off-target activities are identified using domain insertion profiling inside SpCas9 and with different combinations of TadA variants. Engineered ABE variants in this study display narrowed, expanded or shifted editing scopes with efficient editing activities across protospacer positions 2-16.

Identifying age-specific gene signatures of the human cerebral cortex with joint analysis of transcriptomes and functional connectomes.
Zhao X, Chen J, Xiao P, Feng J, Nie Q, Zhao XM.
Briefings in Bioinformatics. (2020)

Here, with a novel method transcriptome-connectome correlation analysis (TCA), which integrates the brain functional magnetic resonance images and region-specific transcriptomes, we identify age-specific cortex (ASC) gene signatures for adolescence, early adulthood and late adulthood.

nMAGMA: a network-enhanced method for inferring risk genes from GWAS summary statistics and its application to schizophrenia.
Yang A, Chen J, Zhao XM.
Briefings in Bioinformatics. (2020)

We propose a new approach, namely network-enhanced MAGMA (nMAGMA), for gene-wise annotation of variants from GWAS summary statistics. Compared with MAGMA and H-MAGMA, nMAGMA significantly extends the lists of genes that can be annotated to SNPs by integrating local signals, long-range regulation signals (i.e. interactions between distal DNA elements), and tissue-specific gene networks.

Macrel: antimicrobial peptide screening in genomes and metagenomes.
Santos-Júnior CD, Pan S, Zhao XM, Coelho LP.
PeerJ. (2020)

Here, we present Macrel (for metagenomic AMP classification and retrieval), which is an end-to-end pipeline for the prospection of high-quality AMP candidates from (meta)genomes. For this, we introduce a novel set of 22 peptide features. These were used to build classifiers which perform similarly to the state-of-the-art in the prediction of both antimicrobial and hemolytic activity of peptides, but with enhanced precision (using standard benchmarks as well as a stricter testing regime).

Deep learning of brain magnetic resonance images: A brief review.
Zhao X, Zhao XM.
Methods. (2020)

In this survey, we give a brief review of the recent popular deep learning approaches and their applications in brain MRI analysis. Furthermore, popular brain MRI databases and deep learning tools are also introduced. The strength and weaknesses of different approaches are addressed, and challenges as well as future directions are also discussed.

Oxidized Glutathione Increases Delta-Subunit Expressing Epithelial Sodium Channel Activity in Xenopus laevis Oocytes.
Grant GJ, Coca C, Zhao XM, Helms MN.
Emed Res. (2020)

Western blot and PCR analysis show that human small airway epithelial cells (hSAEC) express canonical αβγ-subunits alongside δ-ENaC subunits. Differences in single channel responses to GSSG in hSAECs indicate that airway epithelia redox sensitivity may depend on whether δ- or α- subunits assemble in the membrane.

DeepTL-Ubi: A novel deep transfer learning method for effectively predicting ubiquitination sites of multiple species.
Yu Liu, Ao Li, Zhao XM, Minghui Wang.
Methods. (2020)

In this paper, we propose a novel transfer deep learning method, named DeepTL-Ubi, for predicting ubiquitination sites of multiple species.

scTSSR: gene expression recovery for single-cell RNA sequencing using two-side sparse self-representation.
YJin K, Ou-Yang L, Zhao XM, Yan H, Zhang XF.
Bioinformatics. (2019)

In this paper, we develop an imputation method, called scTSSR, to recover gene expression for scRNA-seq. Unlike most existing methods that impute dropout events by borrowing information across only genes or cells, scTSSR simultaneously leverages information from both similar genes and similar cells using a two-side sparse self-representation model. `

GMrepo: a database of curated and consistently annotated human gut metagenomes.
Wu S, Sun C, Li Y, Wang T, Jia L, Lai S, Yang Y, Luo P, Dai D, Yang YQ, Luo Q, Gao NL, Ning K, He LJ, Zhao XM, Chen WH.
Nucleic Acids Res. (2020)

GMrepo (data repository for Gut Microbiota) is a database of curated and consistently annotated human gut metagenomes. Its main purpose is to facilitate the reusability and accessibility of the rapidly growing human metagenomic data.

What Is the Link Between Attention-Deficit/Hyperactivity Disorder and Sleep Disturbance? A Multimodal Examination of Longitudinal Relationships and Brain Structure Using Large-Scale Population-Based Cohorts.
Shen C, Luo Q, Chamberlain SR, Morgan S, Romero-Garcia R, Du J, Zhao X, Touchette É, Montplaisir J, Vitaro F, Boivin M, Tremblay RE, Zhao XM, Robaey P, Feng J, Sahakian BJ.
Biol Psychiatry. (2020)

This study indicates that ADHD symptoms and sleep disturbances have common neural correlates, including structural changes of the ventral attention system and frontostriatal circuitry. Leveraging data from large datasets, these results offer new mechanistic insights into this clinically important relationship between ADHD and sleep impairment, with potential implications for neurobiological models and future therapeutic directions.

Host DNA Contents in Fecal Metagenomics as a Biomarker for Intestinal Diseases and Effective Treatment.
Jiang P, Lai S, Wu S, Zhao XM, Chen WH.
BMC Genomics. (2020)

Together, we revealed that association between HDCs and gut dysbiosis, and identified HDC as a novel biomarker from fecal metagenomics for diagnosis and effective treatment of intestinal diseases; our results also suggested that host-derived contents may have greater impact on gut microbiota than previously anticipated.

A Graph Regularized Generalized Matrix Factorization Model for Predicting Links in Biomedical Bipartite Networks.
Zhang ZC, Zhang XF, Wu M, Ou-Yang L, Zhao XM, Li XL.
Bioinformatics. (2020)

In this study, we propose a new link prediction method, named graph regularized generalized matrix factorization (GRGMF), to identify potential links in biomedical bipartite Networks.We conduct extensive experiments on six real datasets. Experiment results show that GRGMF can achieve competitive performance on all these datasets, which demonstrate the effectiveness of GRGMF in prediction potential links in biomedical bipartite networks.

Selected publications in 2019

Predicting drug-disease associations with heterogeneous network embedding.
Yang K, Zhao X, Waxman D, Zhao XM.
Chaos. (2019)

In this paper, we propose a method, namely HED (Heterogeneous network Embedding for Drug-disease association), to predict potential associations between drugs and diseases based on a drug-disease heterogeneous network. Specifically, with the heterogeneous network constructed from known drug-disease associations, HED employs network embedding to characterize drug-disease associations and then trains a classifier to predict novel potential drug-disease associations.

Hierarchical graphical model reveals HFR1 bridging circadian rhythm and flower development in Arabidopsis thaliana.
Duren Z, Wang Y, Wang J, Zhao XM, Lv L, Li X, Liu J, Zhu XG, Chen L, Wang Y.
NPJ Syst Biol Appl. (2019)

Here, we proposed a hierarchical graphical model to estimate TF activity from mRNA expression by building TF complexes with protein cofactors and inferring TF’s downstream regulatory network simultaneously. Then we applied our model on flower development and circadian rhythm processes in Arabidopsis thaliana.

EnImpute: imputing dropout events in single cell RNA sequencing data via ensemble learning.
Zhang XF, Ou-Yang L, Yang S, Zhao XM, Hu X, Yan H.
Bioinformatics. (2019)

Imputation of dropout events that may mislead downstream analyses is a key step in analyzing single-cell RNA-sequencing (scRNA-seq) data. We develop EnImpute, an R package that introduces an ensemble learning method for imputing dropout events in scRNA-seq data. EnImpute combines the results obtained from multiple imputation methods to generate a more accurate result.

Identification of Functional Gene Modules Associated With STAT-Mediated Antiviral Responses to White Spot Syndrome Virus in Shrimp.
Zhu G, Li S, Wu J, Li F, Zhao XM.
Frontiers in Physiology (2019)

In this work, based on the gene expression profiles of shrimp with an injection of WSSV and STAT double strand RNA (dsRNA), we constructed a gene co-expression network for shrimp and identified the gene modules that are possibly responsible for STAT-mediated antiviral responses.

DeepPhos: prediction of protein phosphorylation sites with deep learning.
Luo F, Wang M1, Liu Y, Zhao XM, Li A.
Bioinformatics (2019)

In this study we present DeepPhos, a novel deep learning architecture for prediction of protein phosphorylation. Unlike multi-layer convolutional neural networks, DeepPhos consists of densely connected convolutional neuron network blocks which can capture multiple representations of sequences to make final phosphorylation prediction by intra block concatenation layers and inter block concatenation layers.

Selected publications in 2018

Victors: a web-based knowledge base of virulence factors in human and animal pathogens.
Sayers S, Li L, Ong E, Deng S, Fu G, Lin Y, Yang B, Zhang S, Fa Z, Zhao B, Xiang Z, Li Y, Zhao XM, Olszewski MA, Chen L, He Y.
Nucleic Acids Research (2018)

Victors (http://www.phidias.us/victors) is a novel, manually curated, web-based integrative knowledge base and analysis resource for VFs of pathogens that cause infectious diseases in human and animals.

Joint Learning of Multiple Differential Networks With Latent Variables.
Ou-Yang L, Zhang XF, Zhao XM, Wang DD, Wang FL, Lei B, Yan H.
IEEE Transactions on Cybernetics (2018)

In this paper, we propose a joint differential network analysis (JDNA) model to jointly estimate multiple differential networks with latent variables from multiple data sets.

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 u