The aim of Cui Lab is developing methods of bioinformatics and systems biology to investigate biomedical sciences or provide helps for biologists and medical scientists in their studies. Currently, we focus on developing bioinformatic methods of noncoding RNAs and network pharmacology for complex diseases such as cardiovascular diseases.

Currently we mainly focus on:

Lab Head:

Qinghua Cui（崔庆华, 崔慶華）
Ph.D, Professor, PI

Lab Members:

Bitao Zhong PhD student(2021-present)
Wenjie Huang PhD student(2021-present)
Shihao Shao PhD student(2021-present)
Yingyu Lu PhD student(2021-present)
Xiao Lin PhD student(2021-present)
Rui Fan PhD student(2019-present)
Xiangwen Ji PhD student(2019-present)
Chunmei Cui PhD student(2019-present)
Ruya Sun PhD student(2019-present)
Yiran Zhou PhD student(2017-present)

Alumni:

Chengxiang Qiu Master student(2009-2012), now a Ph.D student in University of Washington.
Dong Wang Master student(2010-2013), now an investment manager in Harvest Fund.
Geng Chen Master student(2011-2014), now a staff at Fujian Hospital for Infection disease.
Jian Tu Master student(2012-2015), now a founder CEO of BYSX Co.Ltd.
Jianwei Li Visiting scholar from Hebei University of Technology (2013-2014), now a full professor and the director of the Institute of Computational Medicine.
Chuanbo Huang Visiting scholar from HuaQiao University.(2014-2015)
Changyu Tao joint PhD student (2015), now a PhD student supervised by Prof. Ence Yang.
Junpei Wang Master student (2013-2016), now a PhD student.
Junyi Wang visiting student, now a PhD student at School of Enineering, Peking University.
Zhenzhen Chen PhD student (2014-2017), now an assistant professor and Co-PI in the National Key Lab of Cardiovascular Disease, Fuwai Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College (Beijing, China).
Yuchen Wang PhD student (2013-2017), now an assistant professor in the Institute of Materia Medica, Chinese Academy of Medical Sciences & Peking Union Medical College (Beijing, China).
Wei Ma PhD student (2013-2017), now an assistant professor in the PLA NAVY GENERAL HOSPITAL.
Yuan Zhou Post-Doctor(2015-2017), now a tenure-track PI in the Department of Biomedical Informatics, School of Basic Medical Sciences, Peking University.
Pan Zeng PhD student(2014-2018), now a post-doctor supervised by Prof. Edwin Wang, the AISH Chair Professor at University of Calgary, Canada.
Sisi Guo PhD student(2015-2018), now a manager officer at AstraZeneca Head Office, New York, USA.
Jiangcheng Shi Master student(2015-2018), now a PhD student at Cui Lab.
Xiaowen Feng PhD student(2015-2019), now a postdoc at Harvard University.
Junpei Wang PhD student(2016-2019) & Master student (2014-2016), now an assisstant professor at Capital Medical University.
Weili Yang PhD student(2016-2019), now an assisstant professor at Capital Medical University.
Chunmei Cui Master student(2016-2019), now a PhD student at Cui Lab.
Yan Huang visiting Master student(2017-2018), now a PhD student at China Agricultural University.
Yuanxu Gao PhD student(2017-2020), now a postdoc at Macau University of Science and Technology.
Kaiwen Jia PhD student(2017-2020), now an assistant investigator at the Shanghai Institute of Biochemistry and Cell Biology, CAS.
Chuanbo Huang PhD student(2018-2021), now an assistant prof at Huaqiao University.
Jiangcheng Shi PhD student(2018-2021), now an associate prof at Tianjin University of Technology.
Zhou Huang PhD student(2018-2021), now an associate Investigator at Daping Hospital, Army Medical University.
Wanqing Zhao Master student(2018-2021), now a technician at Fuwai Hospital Shenzhen Campus.

  • Cepred

    • Resource type: Web query-driven and downloadable software.
    Description: Cepred is a tool to predict whether a human intronic miRNA is high co-expressed with its host gene or not based on data of genome coordinate, which can be further used to identify the potential functions, biological processes, phenotypes and diseases associated with this miRNA (Wang et al., PLoS one 2009).

  • TAM

    • Resource type: Web query-driven software.
    Description: TAM is a web-accessible program that is in the first version. For a given list of miRNAs, for example the deregulated miRNAs from microarray or deep sequencing experiments, TAM is able to identify enriched miRNA sets and explore novel miRNAs related to the input, and therefore is able to predict novel miRNAs for given function and diseases(Lu et al., BMC Bioinformatics 2010).

  • TAM 2.0

    • Resource type: Web query-driven software.
    Description: TAM 2.0 is the updated web server of our previously published miRNA set enrichment analysis tool, TAM in 2010. Through manual curation of over 9,000 papers, a more than two-fold growth of reference miRNA sets has been achieved in comparison with previous TAM. We grouped miRNAs into six categories of miRNA sets. And new functions for miRNA set query and result visualization are also enabled in the TAM 2.0. TAM 2.0 provides a tool to mine the functional and disease implication behind miRNAs of interests.

  • sTAM

    • Resource type: Web query-driven software.
    Description: sTAM is a computational tool for single sample miRNA set enrichment analysis. Given miRNA expression profiles and reference miRNA sets, sTAM can calculate a score for each sample in each miRNA set, which can be used to discover disease-related biomarkers at a miRNA-set level, making an important supplement to the single-miRNA level analysis(Shi et al., Molecular Therapy-Nucleic Acids 2020).

  • wTAM

    • Resource type: Web query-driven software.
    Description: wTAM is an improved tool of TAM 2.0, which considers the significant differences of human miRNA importance and introduces miRNA importance scores to miRNA set enrichment analysis. wTAM provides diverse miRNA importance scores to explore the function and disease associations for input miRNA list. (Bioinformatics Advances 2022).

  • MISIM

    • Resource type: Web query-driven software.
    Description: MISIM is a tool for the miRNA functional similarity measuring and network construction. Given a miRNA list, MISIM measures the pairwise functional similarity of the given miRNAs. MISIM is also able to generate a miRNA functional network according to the calculated MISIM similarity coefficients for given miRNAs. (Wang et al., Bioinformatics 2010)

  • Drawing of Chromosomes

    • Resource type: Downloadable R source code.
    Description: This R script is able to draw figures of chromosomes with highlighted genes in various colors. (Wang et al., PLoS ONE 2010)

  • Identification of network components

    • Resource type: Downloadable Java source code.
    Description: The java source code is used to identify network components for a given list of network nodes. It is able to identify not only the network components for the whole network but also can identify network components for a subset of network nodes. (Cui Q. PLoS ONE 2010)

  • miREnvironment

    • Resource type: Web query-driven database and software.
    Description: miREnvironment integrated the experimentally supported interactions of miRNA and environmental factors including drugs. Phenotypes are also curated. Currently, two tools that can predict the therapy outcome of cancer treatment and predict environmental factor-disease relationships are also integrated. (Yang et al., Bioinformatics 2011; Qiu et al., Scientific Reports 2012).

  • miRUPnet

    • Resource type: Web query-driven software.
    Description: miRUPnet is a tool to predict the function of a miRNA based on functional enrichment analysis of its upstream interacting context. (Qiu et al., Molecular BioSystems 2012).

  • miR2Gene

    • Resource type: Web query-driven software.
    Description: miR2Gene is a tool to predict the function of protein-coding gene by miRNA set enrichment analysis of miRNAs that regulate the gene. (Qiu et al., BMC Systems Biology 2011).

  • PPUS

    • Resource type: Web query-driven software.
    Description: PPUS is a tool to predict pseudouridine sites recognized by pseudouridine synthase in RNA. Currently, PPUS can predict pseudouridine sites recognized by PUS1, PUS4 and PUS7 in yeast and PUS4 in human. PPUS employed support vector machine as the classifier and used nucleotides around pseudouridine site as feature. . (Li et al., Bioinformatics 2015).

  • LncRNADisease

    • Resource type: Web query-driven database and software.
    Description: LncRNADisease is a database that integrated experimentally supported long noncoding RNA (lncRNA) and disease associations, and lncRNA interactions. LncRNADisease is also a software platform that integrated tools for prediction novel relationship between lncRNAs and diseases. On June 2018, LncRNADisease 2.0 was released. Recently, LncRNADisease 3.0 has been released.

  • LncTar

    • Resource type: Standalone software and Web query-driven software.
    Description: LncTar is an efficient tool for predicting the RNA targets of long noncoding RNAs (lncRNAs). LncTar can run under Windows and Linux. LncTar shows the following advantages: 1) it has no limits to RNA size. As we know, it can process the largest lncRNAs and mRNAs in current human genome; 2) it runs fast. LncTar can predict one RNA-RNA interaction ~10 seconds on average on a PC; 3) it presents a quantitative standard to automatically evaluate whether two RNAs can interact with each other; 4) it shows a high accuracy. LncTar achieved an accuracy of 80% to the predictions of current experimentally supported mRNA targets of lncRNAs (Li et al., Brief Bioinform 2014).

  • Rsite2

    • Resource type: Web query-driven software.
    Description: Rsite2 is a secondary structure based computational method to predict the functional sites of noncoding RNAs (ncRNAs), which represent a big class of RNA molecules that show critical regulatory functions in a variety of biological processes. (Zeng et al., Scientific Reports 2016).

  • LncDisease

    • Resource type: Standalone software.
    Description: LncDisease is a novel computational method and tool to predict the associations between lncRNAs and diseases (Wang et al., NAR 2016).

  • SRAMP

    • Resource type: Web query-driven software.
    Description: SRAMP would serve as a useful tool to predict m6A modification sites on the RNA sequences (Zhou et al., NAR 2016).

  • MicroPattern

    • Resource type: Web query-driven software.
    Description: MicroPattern is a tool used to mine regular rules and patterns behind a list of microbes. MicroPattern can also be used to calculate the similarity between the given disease-associated microbe list and the collected micro-disease association dataset (Sci Rep. 2017).

  • miES

    • Resource type: Web query-driven software.
    Description: Evaluating the essentiality or importance represents one key step to discover critical miRNAs in diseases (including diagnosis/therapy ). miES is a tool to predict the essentiality or importance of miRNAs with machine learning and sequence features. Moreover, miES score can be used to identify candidate miRNAs from miRNA transcriptome data, to discover hotspot miRNAs, and dissect the miRNAs with cross-species difference in essentiality etc (Bioinformatics 2018).

  • GIC (Gene Importance Calculator)

    • Resource type: Web query-driven software.
    Description: Evaluating the essentiality or importance represents one key step to discover critical mRNAs and long noncoding RNAs (lncRNAs) in diseases (including diagnosis/therapy ). GIC (Gene Importance Calculator) is a tool to predict the essentiality or importance of protein-coding genes and lncRNAs with machine learning and sequence features. Moreover, we showed that GIC score can be used to identify candidate genes/lncRNAs from transcriptome data, to discover hotspot genes/lncRNAs, and dissect the genes/lncRNAs with cross-species difference in essentiality etc (Frontiers in Genetics-Bioinformatics and Computational Biology 2018).

  • SexBiasedDrug

    • Resource type: A java-based software, no-free-release.
    Description: Tremendous differences between human sexes are universally observed. Therefore, identifying and analyzing the sex-biased genes are becoming basically important for uncovering the mystery of sex differences and personalized medicine. However, scientists do not pay much attention on the problem of sex-difference in medicine. To address this issue, we previously proposed computational methods to identify sex-based genes (mRNAs) (Briefings in Bioinformatics, 2018) and miRNAs (Genomics, Proteomics, and Bioinformatics 2018) from public gene expression data. In addition, a number of interesting patterns and rules were found for these sex-biased mRNAs and miRNAs by data mining and analysis. Moreover, we further proposed a computational method and developed a JAVA-based computer program, SexBiasedDrug, for the large-scale prediction of sex-biased responsive drugs on one given organ/tissue, e.g. heart, vascular, liver, etc (Journal of Translational Medicine 2018). For people who are interested in this method and software, please contact Dr. Qinghua Cui.

  • MISIM v2.0

    • Resource type: Web query-driven software.
    Description: MISIM v2.0 is an updated web server. It improved the original MISIM algorithm by implementing both positive and negative miRNA-disease associations, which suggests MISIM v2.0 scores could be positive or negative. Moreover, MISIM v2.0 achieved an algorithm for novel miRNA-disease prediction based on MISIM v2.0 scores. In addition, MISIM v2.0 provided network visualization and functional enrichment analysis for functionally paired miRNAs. (Nucleic Acids Res 2019).

  • MIC (microRNA Importance Calculator)

    • Resource type: Web query-driven software.
    Description: MIC is a tool to predict the importance of human miRNAs with machine learning and sequence features. MIC score can be used to identify candidate miRNAs from miRNA transcriptome to discover hotspot miRNAs. Moreover, MIC can estimate how single nucleotide mutants affect the importance of miRNAs, and dissect the miRNAs with cross-species difference in importance. (Advanced Theory and Simulation 2019).

  • PACES

    • Resource type: Web query-driven software.
    Description: PACES is a tool to predict N4-acetylcytidine (ac4C) modification sites on the mRNA sequences (SCIENTIFIC REPORTS 2019).

  • MDCAP

    • Resource type: Downloadable python source code.
    Description: The MDCAP is a computinal model for predicting novel causal miRNA-disease associations. MDCAP would calculate a score for all potential causal miRNA-disease associations. With the score, users could identify most potential causal miRNAs for a given disease or new causal associated disease of a miRNA. (Gao et al. Frontiers in Genetics 2019).

  • SDI (Subcellular Diversity Index) Database

    • Resource type: Web query-driven software.
    Description: A subcellular diversity index (SDI) was introduced to measure the subcellular diversity of genes for eight species, and the SDI shows its ability in predicting essential genes and drug targets (Jia et al., Frontiers in Genetics 2020). Users can query and download SDI related results and source code for calculating SDI via this database.

  • DCHD (Diabetic Coronary Heart Disease) Risk Prediction

    • Resource type: Web query-driven software.
    Description: DCHD Risk Prediction is an online tool to predict the risk of coronary heart disease as a complication of type 2 diabetes mellitus. This tool use only 8 basic indicators to predict the risk probability and it provides a contribution method of analysing the risk contributions of given patient (SCIENTIFIC REPORTS 2020).

  • smORFunction

    • Resource type: Web query-driven database and software.
    Description: A tool for small open reading frame (smORF) and microprotein function prediction, which provides function predictions for 526,443 smORFs in at most 265 models, 48 tissues/cells, and 82 diseases (and normal) (Ji et al. BMC Bioinformatics 2020).

  • tRFTar

    • Resource type: Web query-driven database and software.
    Description: tRNA-derived fragment (tRF) is a novel class of regulatory small non-coding RNAs cleaved from mature tRNAs. Recent evidence has revealed that tRFs can be loaded onto Argonaute (AGO) family proteins to perform post-transcriptional regulations via substantial tRF-target gene interactions (TGIs). tRFTar includes 920,690 predicted TGIs between 12,102 tRFs and 5,688 target genes and further provides two main functions, namely customized TGI search and tRF functional enrichment analysis.

  • FDS (Functional Divergence Score of orthologous gene)

    • Resource type: Web query-driven software.
    Description: FDS is a tool to measure the functional divergence of human and mouse orthologous genes in the context of miRNA regulation. FDS also provides the divergent functions and phenotypes for a human and mouse orthologous gene pair. Moreover, FDS can explore the similarity of gene expression pattern across species and evaluate the possibility of druggable genes. (Briefings in Bioinformatics 2021).

  • WEAT (Weighted Enrichment Analysis Tools)

    • Resource type: Web query-driven software.
    Description: The weighted enrichment analysis method can overcome the issue of treating every gene equally to the conventional method. WEAT is a web implementation of the weighted enrichment analysis method.WEAT not only provides multiple gene essentiality scores for both single and two gene list(s) enrichment analysis but also can visualize the results for publication. (Bioinformatics 2021).

  • CoVIS

    • Resource type: Web query-driven software.
    Description: The COVID-19 pandemic has lasted for nearly 3 years, which still might threaten public health and therefore needs a long term monitoring. CoVIS is an online tool for calculating the importance scores of SARS-CoV-2 variants and monitoring the tendency of death risk of COVID-19. (Cell Death Discovery 2022).

  • GSGP (Gene Somatic Genome Pattern)

    • Resource type: Standalone software and Web query-driven software.
    Description: GSGP is an algorithm for evaluating the contributions of genes to the somatic single base substitution (SBS) signatures. In addition to exploring the cancer aetiology at the sample level, the GSGP can be used for digging deeper to explore which and how much the aetiologies affect the genes (or which and how much the genes are affected by the aetiologies) (Ji et al. Briefings in Bioinformatics 2023).

  • BIC (Base Importance Calculator)

    • Resource type: Web query-driven software.
    Description: As the fundamental unit of a gene and its transcripts, nucleotides have enormous impacts on the gene function and evolution, and thus on phenotypes and diseases. In order to identify the key nucleotides of one specific gene, it is quite crucial to quantitatively measure the importance of each base on the gene. BIC (Base Importance Calculator) is an algorithm to calculate the importance score of each single base based on sequence information of human mRNAs and long noncoding RNAs (lncRNAs). We further revealed that BIC can effectively evaluate the pathogenicity of both genes and single bases, the prognosis of some cancers, and the transmissibility of SARS-CoV-2. (Briefing in Bioinformatics 2023).

  • HMDD

    • Resource type: Web query-driven database.
    Description: Human MicroRNA Disease Database (HMDD) is a database that contains the experimentally supported miRNA-disease association data, which are manually curated from publications. The dysfunction evidence or miRNAs and literature PubMed ID are also given. The first version of HMDD was released on December 2007 (Lu et al., PLoS one 2008). On June 20, 2013, HMDD v2.0 was released. On June 28, 2018, HMDD v3.0 was released. On June 2023, HMDD v4.0 was released.

  • TransmiR

    • Resource type: Web query-driven database.
    Description: TransmiR integrated the experimentally supported transcription factor and miRNA regulatory relations (Wang et al., NAR 2010). On May, 2018, TransmiR v2.0 was released.

  • miREnvironment

    • Resource type: Web query-driven database and software.
    Description: miREnvironment integrated the experimentally supported interactions of miRNA and environmental factors including drugs. Phenotypes are also curated. Currently, two tools that can predict the therapy outcome of cancer treatment and predict environmental factor-disease relationships are also integrated (Yang et al., Bioinformatics 2011).

  • LncRNADisease

    • Resource type: Web query-driven database and software.
    Description: LncRNADisease is a database that integrated experimentally supported long noncoding RNA (lncRNA) and disease associations, and lncRNA interactions. LncRNADisease is also a software platform that integrated tools for prediction novel relationship between lncRNAs and diseases (Chen et al., NAR 2013). On June 2018, LncRNADisease 2.0 was released. On June 2023, LncRNADisease 3.0 was released.

  • HMDAD

    • Resource type: Web query-driven database.
    Description: The Human Microbe-Disease Association Database (HMDAD) is a resource which collected and curated the human microbe-disease association data from microbiota studies (Ma et al., Brief Bioinform 2016).

  • AGD

    • Resource type: Web query-driven database.
    Description: AGD (Aneurysm Gene Database ) is a database that collected genes which are article-supported to be associated with aneurysm in human, rat and mouse. (Sun et al., Database 2018).

  • TF-miRNA-target regulatory network

    • Resource type: Downloadable data in txt format
    Description: No elements are isolated but interact with each other to form complex networks. For gene regulation, there exist several regulatory relationships, they are transcription factors (TFs)-genes, TF-miRNAs, and miRNA-genes. This network integrated currently available and experimentally supported gene regulatory relations and represents the first TF-miRNA-target network, which is a potentially valuable platform in biological and medical sciences (Qiu et al., BMC Systems Biology 2010).

  • Human microRNA oncogenes and tumor suppressors

    • Resource type: Downloadable data
    Description: We curated human microRNA oncogenes and tumor suppressors, and their annotation data (Wang et al., PLoS ONE 2010).

  • microRNA profiling in various cells

    • Resource type: Downloadable data
    Description: We generated miRNA expression profiles in a number of cells (Li et al., Biomed Res International 2013).

    • Shen Y, Gao Y, Shi J, Huang Z, Dai R, Fu Y, Zhou Y, Kong W, Cui Q. MicroRNA-Disease Network Analysis Repurposes Methotrexate for the Treatment of Abdominal Aortic Aneurysm in Mice. Genomics Proteomics Bioinformatics. 2022 Aug 24:S1672-0229(22)00095-X. doi: 10.1016/j.gpb.2022.08.002.
    • Cui C, Cui Q. Importance score of SARS-CoV-2 genome predicts the death risk of COVID-19. Cell Death Discov. 2022 Jul 2;8(1):303.
    • Liu X, Chen Z, Li S, Jin L, Cui X, Cui C, Deng Y, Gao Q, Fan L, Niu Y, Wang W, Cui C, Zhong J, Cui Q, Geng B, Cai J. Pre-miRNA Hsa-Let-7a-2: a Novel Intracellular Partner of Angiotensin II Type 2 Receptor Negatively Regulating its Signals. Int J Biol Sci. 2022 May 1;18(8):3237-3250.
    • Xu W, Cui C, Cui C, Chen Z, Zhang H, Cui Q, Xu G, Fan J, Han Y, Tang L, Targher G, Byrne CD, Zheng MH, Yang L, Cai J, Geng B. Hepatocellular cystathionine γ lyase/hydrogen sulfide attenuates nonalcoholic fatty liver disease by activating farnesoid X receptor. Hepatology. 2022 May 19. doi: 10.1002/hep.32577.
    • Jia Y, Zhang L, Liu Z, Mao C, Ma Z, Li W, Yu F, Wang Y, Huang Y, Zhang W, Zheng J, Wang X, Xu Q, Zhang J, Feng W, Yun C, Liu C, Sun J, Fu Y, Cui Q, Kong W. Targeting macrophage TFEB-14-3-3 epsilon Interface by naringenin inhibits abdominal aortic aneurysm. Cell Discov. 2022 Mar 1;8(1):21.
    • Sun R, Zhou Y, Cui Q. Comparative analysis of aneurysm subtypes associated genes based on protein-protein interaction network. BMC Bioinformatics. 2021 Dec 11;22(1):587.
    • Hu C, Shi J, Chi Y, Yang J, Cui Q. Y/X-Chromosome-Bearing Sperm Shows Elevated Ratio in the Left but Not the Right Testes in Healthy Mice. Life (Basel). 2021 Nov 11;11(11):1219.
    • Zhao CR, Yang FF, Cui Q, Wang D, Zhou Y, Li YS, Zhang YP, Tang RZ, Yao WJ, Wang X, Pang W, Zhao JN, Jiang ZT, Zhu JJ, Chien S, Zhou J. Vitexin inhibits APEX1 to counteract the flow-induced endothelial inflammation. Proc Natl Acad Sci U S A. 2021 Nov 30;118(48):e2115158118.
    • Zhang S, Liu Z, Xie N, Huang C, Li Z, Yu F, Fu Y, Cui Q, Kong W. Pan-HDAC (Histone Deacetylase) Inhibitors Increase Susceptibility of Thoracic Aortic Aneurysm and Dissection in Mice. Arterioscler Thromb Vasc Biol. 2021 Nov;41(11):2848-2850.
    • Mao C, Ma Z, Jia Y, Li W, Xie N, Zhao G, Ma B, Yu F, Sun J, Zhou Y, Cui Q, Fu Y, Kong W. Nidogen-2 Maintains the Contractile Phenotype of Vascular Smooth Muscle Cells and Prevents Neointima Formation via Bridging Jagged1-Notch3 Signaling. Circulation. 2021 Jul 28. doi: 10.1161/CIRCULATIONAHA.120.053361.
    • Cui C, Zhou Y, Cui Q. Defining the functional divergence of orthologous genes between human and mouse in the context of miRNA regulation. Brief Bioinform. 2021 Jul 5:bbab253. doi: 10.1093/bib/bbab253.
    • Fan R, Cui Q. Toward comprehensive functional analysis of gene lists weighted by gene essentiality scores. Bioinformatics. 2021 Jun 25:btab475.
    • Sun R, Cui C, Zhou Y, Cui Q. Comprehensive Analysis of RNA Expression Correlations between Biofluids and Human Tissues. Genes (Basel). 2021 Jun 18;12(6):935.
    • Yang Y, Ma Q, Li Z, Wang H, Zhang C, Liu Y, Li B, Wang Y, Cui Q, Xue F, Ai D, Zhu Y, He J. Harmine alleviates atherogenesis by inhibiting disturbed flow-mediated endothelial activation via protein tyrosine phosphatase PTPN14 and YAP. Br J Pharmacol. 2021 Apr;178(7):1524-1540.
    • Zhu M, Gao J, Lin XJ, Gong YY, Qi YC, Ma YL, Song YX, Tan W, Li FY, Ye M, Gong J, Cui QH, Huang ZH, Zhang YY, Wang XJ, Lan F, Wang SQ, Yuan G, Feng Y, Xu M. Novel roles of an intragenic G-quadruplex in controlling microRNA expression and cardiac function. Nucleic Acids Res. 2021 Mar 18;49(5):2522-2536.
    • Huang C, Zhou Y, Yang J, Cui Q, Li Y. A New Metric Quantifying Chemical and Biological Property of Small Molecule Metabolites and Drugs. Front Mol Biosci. 2020 Dec 15;7:594800.
    • Xu X, Zhou Y, Feng X, Li X, Asad M, Li D, Liao B, Li J, Cui Q, Wang E. Germline genomic patterns are associated with cancer risk, oncogenic pathways, and clinical outcomes. Sci Adv. 2020 Nov 27;6(48):eaba4905.
    • Ji X, Cui C, Cui Q. smORFunction: a tool for predicting functions of small open reading frames and microproteins. BMC Bioinformatics. 2020 Oct 14;21(1):455.
    • Zhou Y, Peng H, Cui Q, Zhou Y. tRFTar: Prediction of tRF-target gene interactions via systemic re-analysis of Argonaute CLIP-seq datasets. Methods. 2020 Oct 9:S1046-2023(20)30220-6.
    • Fan H, Zhang Y, Zhang J, Yao Q, Song Y, Shen Q, Lin J, Gao Y, Wang X, Zhang L, Zhang Y, Liu P, Zhao J, Cui Q, Li JZ, Chang Y. Cold-Inducible Klf9 Regulates Thermogenesis of Brown and Beige Fat. Diabetes. 2020 Dec;69(12):2603-2618.
    • Cui C, Fan J, Zeng Q, Cai J, Chen Y, Chen Z, Wang W, Li SY, Cui Q, Yang J, Tang C, Xu G, Cai J, Geng B. CD4+ T-Cell Endogenous Cystathionine γ Lyase-Hydrogen Sulfide Attenuates Hypertension by Sulfhydrating Liver Kinase B1 to Promote T Regulatory Cell Differentiation and Proliferation. Circulation. 2020 Nov 3;142(18):1752-1769.
    • Fan R, Zhang N, Yang L, Ke J, Zhao D, Cui Q. AI-based prediction for the risk of coronary heart disease among patients with type 2 diabetes mellitus. Sci Rep. 2020 Sep 2;10(1):14457.
    • Ji X, Zhang H, Cui Q. A Panel of Synapse-Related Genes as a Biomarker for Gliomas. Front Neurosci. 2020 Aug 11;14:822. doi: 10.3389/fnins.2020.00822.
    • Shi J, Hu C, Zhou Y, Cui C, Yang J, Cui Q. MicroRNA Profiling in Paired Left and Right Eyes, Lungs, and Testes of Normal Mice. Mol Ther Nucleic Acids. 2020 Jul 10;21:687-695. doi: 10.1016/j.omtn.2020.07.006.
    • Cui C, Huang C, Zhou W, Ji X, Zhang F, Wang L, Zhou Y, Cui Q. AGTR2, one possible novel key gene for the entry of SARS-CoV-2 into human cells. IEEE/ACM Trans Comput Biol Bioinform. 2020 Jul 14;PP. doi: 10.1109/TCBB.2020.3009099. Online ahead of print.
    • Shi J, Cui Q. sTAM: An Online Tool for the Discovery of miRNA-Set Level Disease Biomarkers. Mol Ther Nucleic Acids. 2020 Jul 10;21:670-675. doi: 10.1016/j.omtn.2020.07.004.
    • Yang X, Du X, Ma K, Li G, Liu Z, Rong W, Miao H, Zhu F, Cui Q, Wu S, Li Y, Du J. Circulating miRNAs Related to Long-term Adverse Cardiovascular Events in STEMI Patients: A Nested Case-Control Study. Can J Cardiol. 2020 Mar 20:S0828-282X(20)30273-7. doi: 10.1016/j.cjca.2020.03.018. Online ahead of print.
    • Wang H, Zhou Y, Yin Z, Chen L, Jin L, Cui Q, Xue L. Transcriptome analysis of common and diverged circulating miRNAs between arterial and venous during aging. Aging (Albany NY). 2020 Jun 30;12(13):12987-13004. doi: 10.18632/aging.103385.
    • Meng Y, Xiang R, Yan H, Zhou Y, Hu Y, Yang J, Zhou Y, Cui Q. Transcriptomic landscape profiling of metformin-treated healthy mice: Implication for potential hypertension risk when prophylactically used. J Cell Mol Med. 2020 Jun 11;24(14):8138-50. doi: 10.1111/jcmm.15472.
    • Ji X, Cui Q. Ancient genes can be served as pan-cancer diagnostic and prognostic biomarkers.J Cell Mol Med. 2020 Jun;24(12):6908-6915. doi: 10.1111/jcmm.15347.
    • Chen Z, Liu X, Luo Y, Wang J, Meng Y, Sun L, Chang Y, Cui Q, Yang J. Repurposing Doxepin to Ameliorate Steatosis and Hyperglycemia by Activating FAM3A Signaling Pathway. Diabetes. 2020 Jun;69(6):1126-1139. doi: 10.2337/db19-1038.
    • Xiang R, Chen J, Li S, Yan H, Meng Y, Cai J, Cui Q, Yang Y, Xu M, Geng B, Yang J. VSMC-Specific Deletion of FAM3A Attenuated Ang II-Promoted Hypertension and Cardiovascular Hypertrophy. Circ Res. 2020 Jun 5;126(12):1746-1759. doi: 10.1161/CIRCRESAHA.119.315558.
    • Cui C, Cui Q. The relationship of human tissue microRNAs with those from body fluids. Sci Rep. 2020 Mar 27;10(1):5644. doi: 10.1038/s41598-020-62534-6.
    • Jia K, Zhou Y, Cui Q. Quantifying Gene Essentiality Based on the Context of Cellular Components. Front Genet. 2020 Jan 21;10:1342. doi: 10.3389/fgene.2019.01342.
    • Li J, Huang Y, Cui Q, Zhou Y. m6Acorr: an online tool for the correction and comparison of m6A methylation profiles. BMC Bioinformatics. 2020 Jan 29;21(1):31. doi: 10.1186/s12859-020-3380-6.
    • Jia K, Gao Y, Shi J, Zhou Y, Zhou Y, Cui Q. Annotation and curation of the causality information in LncRNADisease. Database (Oxford). 2020 Jan 1;2020:baz150. doi: 10.1093/database/baz150.
    • Huang Z, Liu L, Gao Y, Shi J, Cui Q, Li J, Zhou Y. Benchmark of computational methods for predicting microRNA-disease associations. Genome Biol. 2019 Oct 8;20(1):202.
    • Gao Y, Jia K, Shi J, Zhou Y, Cui Q. A computational model to predict the causal miRNAs for diseases. Frontiers in Genetics 2019, 02 October,doi: 10.3389/fgene.2019.00935.
    • Zhao W, Zhou Y, Cui Q, Zhou Y. PACES: prediction of N4-acetylcytidine (ac4C) modification sites in mRNA. Sci Rep. 2019 Jul 31;9(1):11112. doi: 10.1038/s41598-019-47594-7.
    • Cui C, Shi B, Shi J, Zhou Y, and Cui Q. Defining the Importance Score of Human MicroRNAs and Their Single Nucleotide Mutants Using Random Forest Regression and Sequence Data. Advanced Theory and Simulation. 2019. doi: 10.1002/adts.201900083.
    • Zhang J, Wang J, Li F, Zhu M, Wang S, Cui Q, Yuan G, Zhou J, Xu M. Normal expression of KCNJ11 is maintained by the G-quadruplex. Int J Biol Macromol. 2019 Jul 17;138:504-510. doi: 10.1016/j.ijbiomac.2019.07.094. [Epub ahead of print]
    • Fan J, Zheng F, Li S, Cui C, Jiang S, Zhang J, Cai J, Cui Q, Yang J, Tang X, Xu G, Geng B. Hydrogen sulfide lowers hyperhomocysteinemia dependent on cystathionine γ lyase S-sulfhydration in ApoE-knockout atherosclerotic mice. Br J Pharmacol. 2019 May 29. doi: 10.1111/bph.14719. [Epub ahead of print]
    • Yang W, Feng B, Meng Y, Wang J, Geng B, Cui Q, Zhang H, Yang Y, Yang J. FAM3C-YY1 axis is essential for TGFβ-promoted proliferation and migration of human breast cancer MDA-MB-231 cells via the activation of HSF1. J Cell Mol Med. 2019 May;23(5):3464-3475
    • Li J, Zhang S, Wan Y, Zhao Y, Shi J, Zhou Y, Cui Q. MISIM v2.0: a web server for inferring microRNA functional similarity based on microRNA-disease associations. Nucleic Acids Res. 2019 May 9.pii: gkz328. [Epub ahead of print]
    • Cui A, Fan H, Zhang Y, Zhang Y, Niu D, Liu S, Liu Q, Ma W, Shen Z, Shen L, Liu Y, Zhang H, Xue Y, Cui Y, Wang Q, Xiao X, Fang F, Yang J, Cui Q, Chang Y. Dexamethasone-induced Krüppel-like factor 9 expression promotes hepatic gluconeogenesis and hyperglycemia. J Clin Invest. 2019 Apr 29;130. pii: 66062.
    • Feng X, Wang E, Cui Q. Gene Expression-Based Predictive Markers for Paclitaxel Treatment in ER+ and ER- Breast Cancer. Front Genet. 2019 Mar 1;10:156.
    • Huang Z, Shi J, Gao Y, Cui C, Zhang S, Li J, Zhou Y, and Cui Q. HMDD v3.0: a database for experimentally supported human microRNA-disease associations. Nucleic Acids Res. 2019 Jan 8;47(D1):D1013-D1017.
    • Bao Z, Yang Z, Huang Z, Zhou Y, Zhao Z, Ren Y, Cui Q, and Dong D. LncRNADisease 2.0: an updated database of long non-coding RNA-associated diseases. Nucleic Acids Res. 2019 Jan 8;47(D1):D1034-D1037.
    • Tong Z, Cui Q, Wang J, Zhou Y. TransmiR v2.0: an updated transcription factor-microRNA regulation database. Nucleic Acids Res. 2019 Jan 8;47(D1):D253-D258.
    • Sun X, Lv H, Zhao P, He J, Cui Q, Wei M, Feng S, Zhu Y. Commutative regulation between endothelial NO synthase and insulin receptor substrate 2 by microRNAs. J Mol Cell Biol. 2018 Oct 8. doi: 10.1093/jmcb/mjy055. [Epub ahead of print]
    • Sun R, Cui C, Zhou Y, and Cui Q. AGD: Aneurysm Gene Database. DATABASE. 2018, 1-6. doi: 10.1093/database/bay100.
    • Cui C, Huang C, Liu K, Xu G, Yang J, Zhou Y, Feng Y, Kararigas G, Geng B, and Cui Q. Large-scale in-silico identification of drugs exerting sex-specific effects in the heart. Journal of Translational Medicine. 2018 Aug 29;16(1):236.
    • Zeng P, Chen J, Meng Y, Zhou Y, Yang J, and Cui Q. Defining essentiality score of protein-coding genes and long noncoding RNAs. Frontiers in Genetics. 2018, 9:380.
    • Song F, Cui C, Gao L, and Cui Q. miES: predicting the essentiality of miRNAs with machine learning and sequence features. Bioinformatics. 2018 Aug 28. doi: 10.1093/bioinformatics/bty738.
    • Jia K, Cui C, Gao Y, Zhou Y, and Cui Q. An analysis of aging-related genes derived from the Genotype-Tissue Expression (GTEx) project. Cell Death Discovery. 2018 Aug 20;5:26.
    • Zheng F, Han J, Lu H, Cui C, Yang J, Cui Q, Cai J, Zhou Y, Tang C, Xu G, Geng B. Cystathionine beta synthase-hydrogen sulfide system in paraventricular nucleus reduced high fatty diet induced obesity and insulin resistance by brain-adipose axis. Biochim Biophys Acta. 2018 Jul 20. pii: S0925-4439(18)30257-6. doi: 10.1016/j.bbadis.2018.07.014. [Epub ahead of print]
    • Zhou Yiran, Cui Q, and Zhou Yuan. NmSEER: a prediction tool for 2’-O-methylation (Nm) sites based on random forest. has been accepted for oral presentation at the 2018 International Conference on Intelligent Computing, which has been selected into the following Springer-Nature volume: Lecture Notes in Computer Science (LNCS). At the same time, it has also been sub-selected into the following journal: BMC Bioinformatics.
    • Cui C, Yang W, Shi J, Zhou Y, Yang J, Cui Q, and Zhou Y. Identification and Analysis of Human Sex-biased MicroRNAs. Genomics Proteomics Bioinformatics. 2018 Jul 11. pii: S1672-0229(18)30130-X. doi: 10.1016/j.gpb.2018.03.004. [Epub ahead of print]
    • Xu H, Wang Y, Lin S, Deng W, Peng D, Cui Q, and Xue Y. PTMD: A Database of Disease-associated Post-translational modifications. Genomics, Proteomics & Bioinformatics. 2018 Sep 20. pii: S1672-0229(18)30318-8.
    • Huang C, Yang W, Wang J, Zhou Y, Geng B, Kararigas G, Yang J, and Cui Q. The DrugPattern tool for drug set enrichment analysis and its prediction for beneficial effects of oxLDL on type 2 diabetes. J Genet Genomics. 2018 Jul 24. pii: S1673-8527(18)30122-X. doi: 10.1016/j.jgg.2018.07.002. [Epub ahead of print]
    • Ma W, Chen J, Meng Y, Yang J, Cui Q, and Zhou Y. Metformin alters gut microbiota of healthy mice: implication for its potential role in gut microbiota homeostasis. Front Microbiol. 2018 Jun 22;9:1336. doi: 10.3389/fmicb.2018.01336. eCollection 2018.
    • Li J, Han X, Wan Y, Zhang S, Zhao Y, Fan R, Cui Q, Zhou Y. TAM 2.0: tool for MicroRNA set analysis. Nucleic Acids Res. 2018 Jun 6. doi: 10.1093/nar/gky509. [Epub ahead of print]
    • Zhou Y, Cui Q. Comparative Analysis of Human Genes Frequently and Occasionally Regulated by m6A Modification. Genomics, Proteomics & Bioinformatics. 2018 Apr;16(2):127-135. doi: 10.1016/j.gpb.2018.01.001. Epub 2018 May 3.
    • Guo J, Zhou Y, Cheng Y, Fang W, Hu G, Wei J, Lin Y, Man Y, Guo L, Sun M, Cui Q, Li J. Metformin-Induced Changes of the Coding Transcriptome and Non-Coding RNAs in the Livers of Non-Alcoholic Fatty Liver Disease Mice. Cell Physiol Biochem. 2018;45(4):1487-1505. doi: 10.1159/000487575. Epub 2018 Feb 16.
    • Wang J, Yang W, Chen Z, Chen J, Meng Y, Feng B, Sun L, Dou L, Li J, Cui Q, Yang J. Long non-coding RNA LncSHGL recruits hnRNPA1 to suppress hepatic gluconeogenesis and lipogenesis. Diabetes. 2018 Apr;67(4):581-593. doi: 10.2337/db17-0799. Epub 2018 Jan 30.
    • Du C, Lin X, Xu W, Zheng F, Cai J, Yang J, Cui Q, Tang C, Cai J, Xu G, Geng B.Sulfhydrated sirtuin-1 increasing its deacetylation activity is an essential epigenetics mechanism of anti-atherogenesis by hydrogen sulfide. Antioxid Redox Signal. 2018 Feb 26. doi: 10.1089/ars.2017.7195.
    • Jin L, Lin X, Yang L, Fan X, Wang W, Li S, Li J, Liu X, Bao M, Cui X, Yang J, Cui Q, Geng B, Cai J.AK098656, a Novel Vascular Smooth Muscle Cell-Dominant Long Noncoding RNA, Promotes Hypertension.Hypertension. 2018 Feb;71(2):262-272.
    • Chen Z, Wang J, Yang W, Chen J, Meng Y, Feng B, Chi Y, Geng B, Zhou Y, Cui Q, Yang J. FAM3C activates HSF1 to suppress hepatic gluconeogenesis and attenuate hyperglycemia of type 1 diabetic mice.Oncotarget. 2017 Nov 20;8(62):106038-106049.
    • Chen Z, Wang J, Yang W, Chen J, Meng Y, Geng B, Cui Q, Yang J. FAM3A mediates PPARγ's protection in liver ischemia-reperfusion injury by activating Akt survival pathway and repressing inflammation and oxidative stress. Oncotarget. 2017 Jul 25;8(30):49882-49896.
    • Zhu Y, Xiong K, Shi J, Cui Q, Xue L. A potential role of microRNAs in protein accumulation in cellular senescence analyzed by bioinformatics. PLoS One. 2017 Jun 7;12(6):e0179034.
    • Liu X, Zeng P, Cui Q, Zhou Y. Comparative analysis of genes frequently regulated by drugs based on connectivity map transcriptome data. PLoS One. 2017 Jun 2;12(6):e0179037.
    • Chi Y, Li J, Li N, Chen Z, Ma L, Peng W, Pan X, Li M, Yu W, He X, Geng B, Cui Q, Liu Y, Yang J. FAM3A enhances adipogenesis of 3T3-L1 preadipocytes via activation of ATP-P2 receptor-Akt signaling pathway.Oncotarget. 2017 Jul 11;8(28):45862-45873.
    • Yang W, Wang J, Chen Z, Chen J, Meng Y, Chen L, Chang Y, Geng B, Sun L, Dou L, Li J, Guan Y, Cui Q, Yang J. NFE2 Induces miR-423-5p to Promote Gluconeogenesis and Hyperglycemia by Repressing the Hepatic FAM3A-ATP-Akt Pathway.Diabetes. 2017 Jul;66(7):1819-1832.
    • Chen Z, Ding L, Yang W, Wang J, Chen L, Chang Y, Geng B, Cui Q, Guan Y, Yang J. Hepatic Activation of the FAM3C-HSF1-CaM Pathway Attenuates Hyperglycemia of Obese Diabetic Mice.Diabetes. 2017 May;66(5):1185-1197.
    • Xu W, Zhou Y, Xu G, Geng B, Cui Q. Transcriptome analysis reveals non-identical microRNA profiles between arterial and venous plasma. Oncotarget. 2017 Feb 14. doi: 10.18632/oncotarget.15310.
    • Zheng Y, Liao F, Lin X, Zheng F, Fan J, Cui Q, Yang J, Geng B, Cai J. Cystathionine γ-Lyase-Hydrogen Sulfide Induces Runt-Related Transcription Factor 2 Sulfhydration, Thereby Increasing Osteoblast Activity to Promote Bone Fracture Healing.Antioxid Redox Signal. 2017 Oct 10;27(11):742-753.
    • Li J, Zhao F, Wang Y, Chen J, Tao J, Tian G, Wu S, Liu W, Cui Q, Geng B, Zhang W, Weldon R, Auguste K, Yang L, Liu X, Chen L, Yang X, Zhu B, Cai J. Gut microbiota dysbiosis contributes to the development of hypertension.Microbiome. 2017 Feb 1;5(1):14.
    • Ma W, Huang C, Zhou Y, Li J, Cui Q. MicroPattern: a web-based tool for microbe set enrichment analysis and disease similarity calculation based on a list of microbes. Sci Rep. 2017 Jan 10;7:40200. doi: 10.1038/srep40200.
    • Guo S, Zhou Y, Zeng P, Xu G, Wang G, and Cui Q. Identification and analysis of the human sex-biased genes. Briefings in Bioinformatics 2016 Dec 27. pii: bbw125. doi: 10.1093/bib/bbw125. [Epub ahead of print].
    • Fu Y, Gao C, Liang Y, Wang M, Huang Y, Ma W, Li T, Jia Y, Yu F, Zhu W, Cui Q, Li Y, Xu Q, Wang X, Kong W. Shift of Macrophage Phenotype Due to Cartilage Oligomeric Matrix Protein Deficiency Drives Atherosclerotic Calcification.Circ Res. 2016 Jul 8;119(2):261-76.
    • Fang L, Zhang M, Li Y, Liu Y, Cui Q, Wang N. PPARgene: A Database of Experimentally Verified and Computationally Predicted PPAR Target Genes.PPAR Res. 2016;2016:6042162.
    • Zhou Y, Zeng P, Li Y, Zhang Z, Cui Q. SRAMP: prediction of mammalian N6-methyladenosine (m6A) sites based on sequence-derived features. Nucleic Acids Res 2016 Jun 2;44(10):e91.
    • Wang J, Ma R, Ma W, Chen J, Yang J, Xi Y, Cui Q. LncDisease: a sequence based bioinformatics tool for predicting lncRNA-disease associations. Nucleic Acids Res 2016 May 19;44(9):e90.
    • Shi B, Guo H, Zhang T, Cui Q. Analysis of plasma microRNA expression profiles revealed different cancer susceptibility in healthy young adult smokers and middle-aged smokers. Oncotarget 2016 Apr 19; 7(16):21676-85.
    • Ma W, Zhang L, Zeng P, Huang C, Li J, Geng B, Yang J, Kong W, Zhou X, and Cui Q. An analysis of human microbe-disease associations. Briefings in Bioinformatics 2016 Feb 15. pii: bbw005. [Epub ahead of print]
    • Zhang C, Lu J, Liu B, Cui Q, Wang Y. Primate-specific miR-603 is implicated in the risk and pathogenesis of Alzheimer's disease. Aging (Albany NY). 2016 Feb 8. [Epub ahead of print]
    • Zeng P, Cui Q. Rsite2: an efficient computational method to predict the functional sites of noncoding RNAs. Sci Rep. 2016 Jan 11;6:19016.
    • Liao F, Zheng Y, Cai J, Fan J, Wang J, Yang J, Cui Q, Xu G, Tang C, Geng B. Catestatin attenuates endoplasmic reticulum induced cell apoptosis by activation type 2 muscarinic acetylcholine receptor in cardiac ischemia/reperfusion. Sci Rep. 2015 Nov 16;5:16590.
    • Zhang K, Li T, Fu Y, Cui Q, Kong W. Predicting Abdominal Aortic Aneurysm Target Genes by Level-2 Protein-Protein Interaction. PLoS One. 2015 Oct 23;10(10):e0140888.
    • Wang Y, Qiu C, Cui Q. A Large-Scale Analysis of the Relationship of Synonymous SNPs Changing MicroRNA Regulation with Functionality and Disease.Int J Mol Sci. 2015 Sep 30;16(10):23545-55.
    • Chen Z, Luo Y, Yang W, Ding L, Wang J, Tu J, Geng B, Cui Q, Yang J. Comparison Analysis of Dysregulated LncRNA Profile in Mouse Plasma and Liver after Hepatic Ischemia/Reperfusion Injury. PLoS One. 2015 Jul 29;10(7):e0133462.
    • Zeng H, Qiu C, Cui Q. Drug-Path: a database for drug-induced pathways. Database (Oxford). 2015 Jun 30;2015:bav061.
    • Li YH, Zhang G, Cui Q. PPUS: a web server to predict PUS-specific pseudouridine sites. Bioinformatics. 2015 Oct 15;31(20):3362-4.
    • Zeng P, Li J, Ma W, Cui Q. Rsite: a computational method to identify the functional sites of noncoding RNAs. Sci Rep. 2015 Mar 17;5:9179.
    • Li J, Ma W, Zeng P, Wang J, Geng B, Yang J, and Cui Q. LncTar: a tool for predicting the RNA targets of long noncoding RNAs. Briefings in Bioinformatics 2015 16(5):806-12.
    • Li J, Gao C, Wang Y, Ma W, Tu J, Wang J, Chen Z, Kong W, and Cui Q. A bioinformatics method for predicting long noncoding RNAs associated with vascular disease. Science China Life Sciences 2014 57(8):852-7.
    • Jin L, Tu J, Jia J, An W, Tan H, Cui Q, and Li Z. Drug-repurposing identified the combination of Trolox C and Cytisine for the treatment of type 2 diabetes. Journal of Translational Medicine 2014 12:153.
    • Jia S, Chen Z, Li J, Chi Y, Wang J, Li S, Luo Y, Geng B, Wang C, Cui Q, Guan Y, Yang J. FAM3A promotes vascular smooth muscle cell proliferation and migration and exacerbates neointima formation in rat artery after balloon injury. J Mol Cell Cardiol 2014 74C:173-182.
    • Liu M, Chen X, Cui Q, and Yan G. A framework to infer the disease associated with human long noncoding RNAs. PLoS ONE 2014 9(1): e84408.
    • Li Y, Qiu C, Tu J, Geng B, Yang J, Jiang T, and Cui Q.HMDD v2. 0: a database for experimentally supported human microRNA and disease associations. Nucleic Acids Research , 2014, 42(D1), D1070-D1074.
    • Li X, Wang J, Jia Z, Cui Q, Zhang C, Wang W, Chen P, Ma K, and Zhou C. MiR-499 Regulates Cell Proliferation and Apoptosis during Late-Stage Cardiac Differentiation via Sox6 and Cyclin D1. PLoS ONE 2013 8(9): e74504.
    • Zheng X, Li A, Zhao L, Zhou T, Shen Q, Cui Q, and Qin X. Key role of microRNA-15a in the KLF4 suppressions of proliferation and angiogenesis in endothelial and vascular smooth muscle cells. Biochemical and Biophysical Research Communications. Volume 437, Issue 4, 9 August 2013, Pages 625-631.
    • Xu W, Geng B, Cui Q, Guan Y, and Yang J. Intracellular and extracellular ATP in regulation of insulin secretion from pancreatic β cells. Journal of Diabetes, 2013 (in press).
    • Li D, Chen G, Yang J, Fan X, Gong Y, Xu G, Cui Q, and Geng B. Transcriptome Analysis Reveals Distinct Patterns of Long Noncoding RNAs in Heart and Plasma of Mice with Heart Failure. PLoS ONE 2013 8(10): e77938.
    • Li Y, Li Z, Zhou S, Wen J, Geng B, Yang J, and Cui Q. Genome-Wide Analysis of Human MicroRNA Stability. BioMed Research International 2013:368975
    • Chen Z, Jia S, Li D, Cai J, Tu J, Geng B, Cui Q, and Yang J. Silencing of Long Noncoding RNA AK139328 Attenuates Ischemia/Reperfusion Injury in Mouse Livers. PLoS ONE 2013 8(11): e80817.
    • Chen G, Wang Z, Wang D, Qiu C, Liu M, Chen X, Zhang Q, Yan G, and Cui Q. LncRNADisease: a database for long noncoding RNA associated diseases. Nucleic Acids Research. 41, 983-986, 2013
    • Chen G, Wang J, and Cui Q. Could circulating microRNAs contribute to cancer therapy. Trends in Molecular Medicine . 2013 19 (2), 71-73.
    • Chen G, Qiu C, Zhang Q, Liu B, and Cui Q. Genome-wide analysis of human SNPs at long intergenic noncoding RNAs. Human Mutation. 2013 34 (2), 338-344.
    • Chen X, Liu M, Cui Q, and Yan G. Prediction of Disease-Related Interactions between MicroRNAs and Environmental Factors Based on a Semi-Supervised Classifier. PLOS ONE 2012, 7(8) e43425 DOI: 10.1371/journal.pone.0043425
    • Qiu C, Chen G, and Cui Q. Towards the understanding of microRNA and environmental factor interactions and their relationships to human diseases. SCIENTIFIC REPORTS 2012:2.DOI: 10.1038/srep00318
    • Qiu C, Wang D, Wang E, and Cui Q. An upstream interacting context based framework for the computational inference of microRNA functions. MOLECULAR BIOSYSTEMS 2012, 8(5) 1492-1498 DOI: 10.1039/c2mb05469h
    • Li X, Gao L, Cui Q, Gary BD, Dyess DL, Taylor W, Shevde-Samant LR, Samant RS, Dean-Colomb W, Piazza GA, Xi Y. Sulindac inhibits tumor cell invasion by suppressing NF-kB mediated transcription of microRNAs. Oncogene 2012 Jan 30
    • Qiu C, Wang J, and Cui Q. miR2Gene: Pattern discovery of single gene, multiple genes, and pathways by enrichment analysis of their microRNA regulators. BMC Systems Biology 2011 5 (Suppl 2):S9.
    • Cui XB, Wang C, Li L, Fan D, Zhou Y, Wu D, Cui QH, Fu FY, Wu LL. Insulin decreases myocardial adiponectin receptor 1 expression via PI3K/Akt and FoxO1 pathway. Cardiovasc Res. 2011 Oct 19. [Epub ahead of print]
    • Wang J，Zhang J，Li K, Zhao W and Cui Q. SpliceDisease database: linking RNA splicing and disease. Nucleic Acids Research, 2011 database issue (in press).
    • Yang Q, Qiu C, Yang J, Wu Q, and Cui Q. miREnvironment Database: providing a bridge for microRNAs, environmental factors, and phenotypes. Bioinformatics 2011 27: 3329-3330.
    • Li W, Rong R, Zhao S, Zhu X, Zhang K, Xiong X, Yu X, Cui Q, Li S, Chen L, Cai J, Du J. Proteomic analysis of metabolic, cytoskeletal, and stress response proteins in human heart failure. J Cell Mol Med. 2011 May 5.
    • Wang J , Li Z, Qiu C, Wang D, and Cui Q. The relationship between rational drug design and drug side effects. Briefings in Bioinformatics. 2011 Sep 23.
    • Cui Q. A Network of Cancer Genes with Co-Occurring and Anti-Co-Occurring Mutations. PLoS ONE 2010 5(10): e13180. doi:10.1371/journal.pone.0013180
    • Wang D, Qiu C, Zhang H, Wang J, Cui Q, and Yin Y. Human microRNA oncogenes and tumor suppressors show significantly different biological patterns: from functions to targets. PLoS ONE 2010 5(9):e13067. doi:10.1371/journal.pone.0013067
    • Lu M, Shi B, Wang J, Cao Q, Cui Q. TAM: A method for enrichment and depletion analysis of a microRNA category in a list of microRNAs. BMC Bioinformatics. 2010, 11:419 (9 Auguest 2010). doi:10.1186/1471-2105-11-419
    • Li J, Lenferink A, Deng Y, Collins C, Cui Q, Purisima E, O'Connor-McCourt M, and Wang E. Identification of high quality cancer prognostic markers and metastasis network modules. Nat. Commun. 2010 1: 34. doi:10.1038/ncomms1033
    • Chang N, Yi J, Guo G, Liu X, Shang Y, Tong T, Cui Q, Zhan M, Gorospe M, Wang W. HuR uses AUF1 as a cofactor to promote p16INK4 mRNA decay. Mol Cell Biol. 2010 May 24. doi:10.1128/MCB.00169-10
    • Qiu C, Wang J, Yao P, Wang E, and Cui Q. microRNA evolution in a human transcription factor and microRNA regulatory network. BMC Systems Biology 2010, 4:90. doi:10.1186/1752-0509-4-90
    • Wang D, Wang, J, Lu M, Song F, and Cui Q. Inferring the human microRNA functional similarity and functional network based on microRNA-associated diseases. Bioinformatics 2010 26: 1644-1650. doi:10.1093/bioinformatics/btq241
    • Qin X, Wang X, Wang Y, Tang Z, Cui Q, Xi J, Li YS, Chien S, Wang N. MicroRNA-19a mediates the suppressive effect of laminar flow on cyclin D1 expression in human umbilical vein endothelial cells. Proc Natl Acad Sci U S A. 2010 Feb 16;107(7):3240-4. doi:10.1073/pnas.0914882107
    • Wang J, Lu M, Qiu C, and Cui Q. TransmiR: a transcription factor-microRNA regulation database. Nucleic Acids Research 2010 doi:10.1093/nar/gkp803
    • Shi G, Cui Q, and Youyi Zhang. MicroRNA Set: A Novel Way to Uncover the Potential Black Box of Chronic Heart Failure in MicroRNA Microarray Analysis. J Comput Sci Syst Biol 2009 2: 240-246. doi:10.4172/jcsb.1000036
    • Wang D, Lu M, Miao J, Li T, Wang E, and Cui Q. Cepred: Predicting the Co-Expression Patterns of the Human Intronic microRNAs with Their Host Genes. PLoS ONE 2009 4(2): e4421. doi:10.1371/journal.pone.0004421
    • Zhao H, Wang D,Liu B, Jiang X, Zhang J, Fan M, Fan Z, Chen Y, Song SW, Gao W, Jiang T, Cui Q. Recombination rates of human microRNA. Biochem Biophys Res Commun. 2009 Feb 13;379(3):702-5. doi:10.1016/j.bbrc.2008.12.144
    • Zhang F, Lu M, Zhang Q, Zhang F, Gao W, and Cui Q. Predicting cardiovascular disease associated microRNAs by bioinformatics. Journal of Peking University(Health Sciences), 2009 41(1): 112-116 (in Chinese)
    • Cui Q, Purisima EO, Wang E, Protein evolution on a human signaling network. BMC Systems Biology 2009, 3:21. doi:10.1186/1752-0509-3-21
    • Miao J, Fan Q, Cui Q, Zhang H, Chen L, Wang S, Guan N, Guan Y, Ding J. Newly identified cytoskeletal components are associated with dynamic changes of podocyte foot processes. Nephrol Dial Transplant 2009, 24(11):3297-3305. doi:10.1093/ndt/gfp338
    • Zhang Q, Lu M, and Cui, Q. SNP analysis reveals an evolutionary acceleration of the human-specific microRNAs. Nature Precedings (2008). doi:10101/npre.2008.2127.1
    • Lu M, Zhang Q, Deng M, Miao J, Guo Y, Gao W, Cui Q. An analysis of human microRNA and disease associations. PLoS ONE. 2008;3(10):e3420. doi:10.1371/journal.pone.0003420
    • Cui Q, Ma Y, Jaramillo M, Bari H, Awan A, Yang S, Zhang S, Liu L, Lu M, O'Connor-McCourt M, Purisima EO, Wang E. A map of human cancer signaling. Mol Syst Biol 2007 3:152. doi:10.1038/msb4100200
    • Cui Q, Yu Z, Purisima EO, Wang E. MicroRNA regulation and interspecific variation of gene expression. Trends Genet 2007 23:372-375. doi:10.1016/j.tig.2007.04.003
    • Cui Q, Yu Z, Pan Y, Purisima EO, Wang E. MicroRNAs preferentially target the genes with high transcriptional regulation complexity. Biochem Biophys Res Commun 2007 352:733-738. doi:10.1016/j.bbrc.2006.11.080
    • Awan A, Bari H, Yoksong S, Chowdhury S, Cui Q, Yu Z, Purisima EO, Wang E. Regulatory network motifs and hotspots of cancer genes in a mammalian cellular signalling network. IET Syst Biol 2007 1: 292-7. doi:10.1049/iet-syb:20060068
    • Cui Q, Yu Z, Purisima EO, Wang E. Principles of microRNA regulation of a human cellular signaling network. Mol Syst Biol 2006 2:46. doi:10.1038/msb4100089
    • Cui Q, Liu B, Jiang T, Ma S. Characterizing the dynamic connectivity between genes by variable parameter regression and Kalman filtering based on temporal gene expression data. Bioinformatics 2005 21:1538-1541. doi:10.1093/bioinformatics/bti197
    • Cui Q, Jiang T, Liu B, Ma S. Esub8: a novel tool to predict protein subcellular localizations in eukaryotic organisms. BMC Bioinformatics 2004 5:66. doi:10.1186/1471-2105-5-66
    • Liu B, Cui Q, Jiang T, Ma S. A combinational feature selection and ensemble neural network method for classification of gene expression data. BMC Bioinformatics 2004 5: 136. doi:10.1186/1471-2105-5-136
    • Huang H, Zhang L, Cui Q, Jiang T, Ma S, Gao Y. Finding Potential Ligands for PDZ Domains by Tailfit, a JAVA program. Chinese Medical Sciences Journal 2004 19(2): 97-104
    • Jiang T, Cui Q, Shi G, Ma S. Protein Folding Simulations of the Hydrophobic-Hydrophilic Model by Combining Tabu Search with Genetic Algorithms. Journal of Chemical Physics 2003 119: 4592-4596. doi:10.1063/1.1592796