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  2022 (5)
Epigenomic and transcriptomic analyses define core cell types, genes and targetable mechanisms for kidney disease. Liu, H.; Doke, T.; Guo, D.; Sheng, X.; Ma, Z.; Park, J.; Vy, H. M. T; Nadkarni, G. N; Abedini, A.; Miao, Z.; and others Nature Genetics, 54(7): 950–962. 2022.
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Genome-wide meta-analysis and omics integration identifies novel genes associated with diabetic kidney disease. Sandholm, N.; Cole, J. B; Nair, V.; Sheng, X.; Liu, H.; Ahlqvist, E.; Van Zuydam, N.; Dahlström, E. H; Fermin, D.; Smyth, L. J; and others Diabetologia,1–15. 2022.
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Linking genetic variants to kidney disease via the epigenome. Liu, H.; and Susztak, K. 2022.
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Spatially resolved human kidney multi-omics single cell atlas highlights the key role of the fibrotic microenvironment in kidney disease progression. Abedini, A.; Ma, Z.; Frederick, J.; Dhillon, P.; Balzer, M. S; Shrestha, R.; Liu, H.; Vitale, S.; Devalaraja-Narashimha, K.; Grandi, P.; and others bioRxiv. 2022.
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Tet2-and Tet3-Mediated Cytosine Hydroxymethylation in Six2 Progenitor Cells in Mice Is Critical for Nephron Progenitor Differentiation and Nephron Endowment. Liang, X.; Aranyi, T.; Zhou, J.; Guan, Y.; Hu, H.; Liu, H.; and Susztak, K. Journal of the American Society of Nephrology. 2022.
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  2021 (11)
The nuclear receptor ESRRA protects from kidney disease by coupling metabolism and differentiation. Dhillon, P.; Park, J.; Del Pozo, C. H.; Li, L.; Doke, T.; Huang, S.; Zhao, J.; Kang, H. M.; Shrestra, R.; Balzer, M. S; and others Cell Metabolism, 33(2): 379–394. 2021.
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Urinary Single-Cell Profiling Captures the Cellular Diversity of the Kidney. Abedini, A.; Zhu, Y. O; Chatterjee, S.; Halasz, G.; Devalaraja-Narashimha, K.; Shrestha, R.; Balzer, M. S; Park, J.; Zhou, T.; Ma, Z.; and others Journal of the American Society of Nephrology, 32(3): 614–627. 2021.
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Single cell regulatory landscape of the mouse kidney highlights cellular differentiation programs and disease targets. Miao, Z.; Balzer, M. S; Ma, Z.; Liu, H.; Wu, J.; Shrestha, R.; Aranyi, T.; Kwan, A.; Kondo, A.; Pontoglio, M.; and others Nature Communications, 12(1): 1–17. 2021.
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Transcriptome-wide association analysis identifies DACH1 as a kidney disease risk gene that contributes to fibrosis. Doke, T.; Huang, S.; Qiu, C.; Liu, H.; Guan, Y.; Hu, H.; Ma, Z.; Wu, J.; Miao, Z.; Sheng, X.; and others The Journal of Clinical Investigation, 131(10). 2021.
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Renal Histologic Analysis Provides Complementary Information to Kidney Function Measurement for Patients with Early Diabetic or Hypertensive Disease. Quinn, G. Z; Abedini, A.; Liu, H.; Ma, Z.; Cucchiara, A.; Havasi, A.; Hill, J.; Palmer, M. B; and Susztak, K. Journal of the American Society of Nephrology, 32(11): 2863–2876. 2021.
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Mapping the genetic architecture of human traits to cell types in the kidney identifies mechanisms of disease and potential treatments. Sheng, X.; Guan, Y.; Ma, Z.; Wu, J.; Liu, H.; Qiu, C.; Vitale, S.; Miao, Z.; Seasock, M. J; Palmer, M.; and others Nature genetics, 53(9): 1322–1333. 2021.
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A single genetic locus controls both expression of DPEP1/CHMP1A and kidney disease development via ferroptosis. Guan, Y.; Liang, X.; Ma, Z.; Hu, H.; Liu, H.; Miao, Z.; Linkermann, A.; Hellwege, J. N; Voight, B. F; and Susztak, K. Nature Communications, 12(1): 1–17. 2021.
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Assessment of differentially methylated loci in individuals with end-stage kidney disease attributed to diabetic kidney disease: an exploratory study. Smyth, L. J; Kilner, J.; Nair, V.; Liu, H.; Brennan, E.; Kerr, K.; Sandholm, N.; Cole, J.; Dahlström, E.; Syreeni, A.; and others Clinical epigenetics, 13(1): 1–19. 2021.
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Genome-wide association studies identify the role of caspase-9 in kidney disease. Doke, T.; Huang, S.; Qiu, C.; Sheng, X.; Seasock, M.; Liu, H.; Ma, Z.; Palmer, M.; and Susztak, K. Science Advances, 7(45): eabi8051. 2021.
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Meta-analyses identify DNA methylation associated with kidney function and damage. Schlosser, P.; Tin, A.; Matias-Garcia, P. R; Thio, C. H.; Joehanes, R.; Liu, H.; Weihs, A.; Yu, Z.; Hoppmann, A.; Grundner-Culemann, F.; and others Nature communications, 12(1): 1–16. 2021.
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Epigenome-wide association study of serum urate reveals insights into urate co-regulation and the SLC2A9 locus. Tin, A.; Schlosser, P.; Matias-Garcia, P. R; Thio, C. H.; Joehanes, R.; Liu, H.; Yu, Z.; Weihs, A.; Hoppmann, A.; Grundner-Culemann, F.; and others Nature communications, 12(1): 1–18. 2021.
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  2020 (2)
Systematic integrated analysis of genetic and epigenetic variation in diabetic kidney disease. Sheng, X.; Qiu, C.; Liu, H.; Gluck, C.; Hsu, J. Y; He, J.; Hsu, C.; Sha, D.; Weir, M. R; Isakova, T.; and others Proceedings of the National Academy of Sciences, 117(46): 29013–29024. 2020.
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Dnmt3a and Dnmt3b-Decommissioned Fetal Enhancers are Linked to Kidney Disease. Guan*, Y.; Liu*, H.; Ma, Z.; Li, S.; Park, J.; Sheng, X.; and Susztak, K. Journal of the American Society of Nephrology, 31(4): 765–782. 2020.
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  2019 (3)
Long-Range Chromatin Interactions in the Kidney. Guan, Y.; Liu, H.; and Susztak, K. 2019.
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Development of highly potent glucocorticoids for steroid-resistant severe asthma. He, Y.; Shi, J.; Nguyen, Q. T.; You, E.; Liu, H.; Ren, X.; Wu, Z.; Li, J.; Qiu, W.; Khoo, S. K.; and others Proceedings of the National Academy of Sciences, 116(14): 6932–6937. 2019.
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The MS-lincRNA landscape reveals a novel lincRNA BCLIN25 that contributes to tumorigenesis by upregulating ERBB2 expression via epigenetic modification and RNA–RNA interactions in breast cancer. Xu*, S.; Liu*, H.; Wan*, L.; Zhang, W.; Wang, Q.; Zhang, S.; Shang, S.; Zhang, Y.; and Pang, D. Cell Death & Disease, 10(12): 1–18. 2019.
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  2018 (1)
Specific breast cancer prognosis-subtype distinctions based on DNA methylation patterns. Zhang, S.; Wang, Y.; Gu, Y.; Zhu, J.; Ci, C.; Guo, Z.; Chen, C.; Wei, Y.; Lv, W.; Liu, H.; and others Molecular Oncology, 12(7): 1047–1060. 2018.
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  2017 (4)
Cell subpopulation deconvolution reveals breast cancer heterogeneity based on DNA methylation signature. Wen*, Y.; Wei*, Y.; Zhang, S.; Li, S.; Liu, H.; Wang, F.; Zhao, Y.; Zhang, D.; and Zhang, Y. Briefings in bioinformatics, 18(3): 426–440. 2017.
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DiseaseMeth version 2.0: a major expansion and update of the human disease methylation database. Xiong*, Y.; Wei*, Y.; Gu*, Y.; Zhang, S.; Lyu, J.; Zhang, B.; Chen, C.; Zhu, J.; Wang, Y.; Liu✉, H.; and others Nucleic acids research, 45(D1): D888–D895. 2017.
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Survival differences of CIMP subtypes integrated with CNA information in human breast cancer. Wang, H.; Yan, W.; Zhang, S.; Gu, Y.; Wang, Y.; Wei, Y.; Liu, H.; Wang, F.; Wu, Q.; and Zhang, Y. Oncotarget, 8(30): 48807. 2017.
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The landscape of DNA methylation-mediated regulation of long non-coding RNAs in breast cancer. Zhang, C.; Wang, X.; Li, X.; Zhao, N.; Wang, Y.; Han, X.; Ci, C.; Zhang, J.; Li, M.; and Zhang, Y. Oncotarget, 8(31): 51134. 2017.
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  2016 (6)
The identification of age-associated cancer markers by an integrative analysis of dynamic DNA methylation changes. Wang*, Y.; Zhang*, J.; Xiao*, X.; Liu, H.; Wang, F.; Li, S.; Wen, Y.; Wei, Y.; Su, J.; Zhang, Y.; and others Scientific reports, 6: 22722. 2016.
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Computational identification of putative lincRNAs in mouse embryonic stem cell. Liu*, H.; Lyu*, J.; Liu*, H.; Gao, Y.; Guo, J.; He, H.; Han, Z.; Zhang, Y.; and Wu✉, Q. Scientific reports, 6: 34892. 2016.
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Identification and functional analysis of long intergenic noncoding RNA genes in porcine pre-implantation embryonic development. Li, J.; Gao, Z.; Wang, X.; Liu, H.; Zhang, Y.; and Liu, Z. Scientific reports, 6(1): 1–9. 2016.
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DNA methylation dynamics: identification and functional annotation. Liu✉, H.; Li, S.; Wang, X.; Zhu, J.; Wei, Y.; Wang, Y.; Wen, Y.; Wang, L.; Huang, Y.; Zhang, B.; and others Briefings in functional genomics, 15(6): 470–484. 2016.
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Systematic identification and annotation of human methylation marks based on bisulfite sequencing methylomes reveals distinct roles of cell type-specific hypomethylation in the regulation of cell identity genes. Liu*✉, H.; Liu*, X.; Zhang*, S.; Lv, J.; Li, S.; Shang, S.; Jia, S.; Wei, Y.; Wang, F.; Su, J.; and others Nucleic acids research, 44(1): 75–94. 2016.
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Folic acid alters methylation profile of JAK-STAT and long-term depression signaling pathways in Alzheimer’s disease models. Li, W.; Liu, H.; Yu, M.; Zhang, X.; Zhang, Y.; Liu, H.; Wilson, J. X; and Huang, G. Molecular neurobiology, 53(9): 6548–6556. 2016.
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  2015 (11)
Chromatin modifications and genomic contexts linked to dynamic DNA methylation patterns across human cell types. Yan*, H.; Zhang*, D.; Liu, H.; Wei, Y.; Lv, J.; Wang, F.; Zhang, C.; Wu, Q.; Su, J.; and Zhang, Y. Scientific reports, 5: 8410. 2015.
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QBioDiff: a web-based tool for quantification and interpretation of biological dif-ferences among multiple samples. Hongbo, L.; Xiaojuan, L.; Shipeng, S.; Yan, Z.; and others Cancer Genetics and Epigenetics, 3. 2015.
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Epigenetic of Somatic Cells Reprogramming. Jie, L.; Yan, H.; Hongbo, L.; Mingming, L.; Yihan, W.; Yan, Z.; and others Cancer Genetics and Epigenetics, 3. 2015.
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Signaling pathway in endometrial carcinoma. Jia, S.; Wei, Y.; Liu, H.; Fu, J.; Wang, F.; and Zhang, Y. Cancer Genetics and Epigenetics, 3(3): 1–4. 2015.
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CellMethy: Identification of a focal concordantly methylated pattern of CpGs revealed wide differences between normal and cancer tissues. Wang, F.; Zhang, S.; Liu, H.; Wei, Y.; Wang, Y.; Han, X.; Su, J.; Zhang, D.; Xie, B.; and Zhang, Y. Scientific reports, 5(1): 1–10. 2015.
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SEA: a super-enhancer archive. Wei*, Y.; Zhang*, S.; Shang*, S.; Zhang, B.; Li, S.; Wang, X.; Wang, F.; Su, J.; Wu, Q.; Liu✉, H.; and others Nucleic acids research, 44(D1): D172–D179. 2015.
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The identification of specific methylation patterns across different cancers. Zhang*, C.; Zhao*, H.; Li, J.; Liu, H.; Wang, F.; Wei, Y.; Su, J.; Zhang, D.; Liu, T.; and Zhang, Y. PloS one, 10(3): e0120361. 2015.
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Identification of 4438 novel lincRNAs involved in mouse pre-implantation embryonic development. Lv, J.; Liu, H.; Yu, S.; Liu, H.; Cui, W.; Gao, Y.; Zheng, T.; Qin, G.; Guo, J.; Zeng, T.; and others Molecular Genetics and Genomics, 290(2): 685–697. 2015.
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DNA methylation patterns can estimate nonequivalent outcomes of breast cancer with the same receptor subtypes. Zhang, M.; Zhang, S.; Wen, Y.; Wang, Y.; Wei, Y.; Liu, H.; Zhang, D.; Su, J.; Wang, F.; and Zhang, Y. PloS one, 10(11). 2015.
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Heterogeneity in Breast cancer. Wen, Y.; Zhang, D.; Liu, H.; Wang, F.; and Zhang, Y. Cancer Genetics and Epigenetics, 3: 1–5. 2015.
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Aberrant DNA methylation and genome instability and mutation in cancer. Li, S; Wen, Y.; Wei, Y.; Wang, Y.; Liu, H.; and others Cancer Genetics and Epigenetics, 3. 2015.
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  2014 (7)
Revealing the architecture of genetic and epigenetic regulation: a maximum likelihood model. Wang, F.; Zhang, S.; Wen, Y.; Wei, Y.; Yan, H.; Liu, H.; Su, J.; Zhang, Y.; and Che, J. Briefings in bioinformatics, 15(6): 1028–1043. 2014.
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Long Non-coding RNAs: key players in brain and central nervous system development. Lv, J.; Liu, H.; Liu, H.; Wu, Q.; and Zhang, Y. Computational Molecular Biology, 4(5). 2014.
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Rice protein regulates HDL metabolism-related gene expression and enzyme activity in adult rats. Li, H.; Yang, L.; Yang, H.; Sun, S.; Liu, H.; Wu, Q.; Chen, J.; and Zhuang, T. Food Bioscience, 8: 1–7. 2014.
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Detection of type 2 diabetes related modules and genes based on epigenetic networks. Liu*, H.; Wang*, T.; Liu, H.; Wei, Y.; Zhao, G.; Su, J.; Wu, Q.; Qiao, H.; and Zhang, Y. BMC systems biology, 8(1): S5. 2014.
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DevMouse, the mouse developmental methylome database and analysis tools. Liu*, H.; Zhu*, R.; Lv*, J.; He, H.; Yang, L.; Huang, Z.; Su, J.; Zhang, Y.; Yu, S.; and Wu, Q. Database, 2014. 2014.
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MetaImprint: an information repository of mammalian imprinted genes. Wei*, Y.; Su*, J.; Liu, H.; Lv, J.; Wang, F.; Yan, H.; Wen, Y.; Liu, H.; Wu, Q.; and Zhang, Y. Development, 141(12): 2516–2523. 2014.
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Identification and characterization of long intergenic non-coding RNAs related to mouse liver development. Lv*, J.; Huang*, Z.; Liu*, H.; Liu*, H.; Cui, W.; Li, B.; He, H.; Guo, J.; Liu, Q.; Zhang, Y.; and others Molecular genetics and genomics, 289(6): 1225–1235. 2014.
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  2013 (8)
Genome-wide identification of Polycomb target genes in human embryonic stem cells. Xiao, X.; Li, Z.; Liu, H.; Su, J.; Wang, F.; Wu, X.; Liu, H.; Wu, Q.; and Zhang, Y. Gene, 518(2): 425–430. 2013.
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Z curve theory-based analysis of the dynamic nature of nucleosome positioning in Saccharomyces cerevisiae. Wu, X.; Liu, H.; Liu, H.; Su, J.; Lv, J.; Cui, Y.; Wang, F.; and Zhang, Y. Gene, 530(1): 8–18. 2013.
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Predicting Long Non-coding RNAs Based on Genomic Sequence Information. Lv, J.; Liu, H.; Liu, H.; Wu, Q.; and Zhang, Y. Computational Molecular Biology, 3(4). 2013.
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Identification of the differential DNA methylation markers among cancers. Liu, H.; Li, Z.; Ding, J.; Liu, J.; and Zhang, Y. In 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pages 730–734, 2013. IEEE
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Long intergenic non-coding RNA detection benefited from integrative modeling of (Epi) genomic data. Lv, J.; Liu, H.; and Wu, Q. In 2013 10th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD), pages 735–740, 2013. IEEE
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Long non-coding RNA identification over mouse brain development by integrative modeling of chromatin and genomic features. Lv*, J.; Liu*, H.; Huang*, Z.; Su, J.; He, H.; Xiu, Y.; Zhang, Y.; and Wu✉, Q. Nucleic acids research, 41(22): 10044–10061. 2013.
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Identification and characterization of long non-coding RNAs related to mouse embryonic brain development from available transcriptomic data. Lv*, J.; Cui*, W.; Liu*, H.; He, H.; Xiu, Y.; Guo, J.; Liu, H.; Liu, Q.; Zeng, T.; Chen, Y.; and others PloS one, 8(8): e71152. 2013.
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Quantitative epigenetic co-variation in CpG islands and co-regulation of developmental genes. Liu*, H.; Chen*, Y.; Lv*, J.; Liu, H.; Zhu, R.; Su, J.; Liu, X.; Zhang, Y.; and Wu, Q. Scientific reports, 3: 2576. 2013.
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  2012 (5)
Advances in bioinformatics tools for high-throughput sequencing data of DNA methylation. Su, J; Huang, D; Yan, H; Liu, H; and Zhang, Y Hereditary Genet, 1(107): 2161–1041. 2012.
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High-throughput computational approaches to analyzing histone modification next-generation sequencing data. Lv, J.; Liu, H.; Wu, Q.; Zhang, Y.; and others Computational Molecular Biology, 2(1). 2012.
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Revealing epigenetic patterns in gene regulation through integrative analysis of epigenetic interaction network. Su*, J.; Qi*, Y.; Liu, S.; Wu, X.; Lv, J.; Liu, H.; Zhang, R.; and Zhang, Y. Molecular biology reports, 39(2): 1701–1712. 2012.
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CpG_MPs: identification of CpG methylation patterns of genomic regions from high-throughput bisulfite sequencing data. Su*, J.; Yan*, H.; Wei*, Y.; Liu, H.; Liu, H.; Wang, F.; Lv, J.; Wu, Q.; and Zhang✉, Y. Nucleic acids research, 41(1): e4–e4. 2012.
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Genome-wide dynamic changes of DNA methylation of repetitive elements in human embryonic stem cells and fetal fibroblasts. Su, J.; Shao, X.; Liu, H.; Liu, S.; Wu, Q.; and Zhang, Y. Genomics, 99(1): 10–17. 2012.
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  2011 (4)
QDMR: a quantitative method for identification of differentially methylated regions by entropy. Zhang*✉, Y.; Liu*, H.; Lv*, J.; Xiao, X.; Zhu, J.; Liu, X.; Su, J.; Li, X.; Wu, Q.; Wang, F.; and others Nucleic acids research, 39(9): e58–e58. 2011.
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DiseaseMeth: a human disease methylation database. Lv*, J.; Liu*, H.; Su*, J.; Wu, X.; Liu, H.; Li, B.; Xiao, X.; Wang, F.; Wu✉, Q.; and Zhang✉, Y. Nucleic acids research, 40(D1): D1030–D1035. 2011.
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Rice protein extracted by different methods affects cholesterol metabolism in rats due to its lower digestibility. Yang, L.; Chen, J.; Xu, T.; Qiu, W.; Zhang, Y.; Zhang, L.; Xu, F.; and Liu, H. International journal of molecular sciences, 12(11): 7594–7608. 2011.
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Prioritizing cancer-related genes with aberrant methylation based on a weighted protein-protein interaction network. Liu*, H.; Su*, J.; Li, J.; Liu, H.; Lv, J.; Li, B.; Qiao, H.; and Zhang, Y. BMC systems biology, 5(1): 158. 2011.
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  2010 (2)
Discovering cooperative relationships of chromatin modifications in human T cells based on a proposed closeness measure. Lv*, J.; Qiao*, H.; Liu, H.; Wu, X.; Zhu, J.; Su, J.; Wang, F.; Cui, Y.; and Zhang, Y. PloS one, 5(12): e14219. 2010.
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Detecting novel hypermethylated genes in Breast cancer benefiting from feature selection. Lv, J.; Su, J.; Wang, F.; Qi, Y.; Liu, H.; and Zhang, Y. Computers in biology and medicine, 40(2): 159–167. 2010.
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  2009 (4)
Deducing causal relationships among different histone modifications, dna methylation and gene expression. Qi, Y.; Zhang, Y.; Lv, J.; Liu, H.; Zhu, J.; and Su, J. In 2009 Fifth International Conference on Natural Computation, volume 6, pages 139–143, 2009. IEEE
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ChIP-seq data plays an important role in a cytosine-based DNA methylation prediction model. Lv, J.; Zhang, Y.; Qi, Y.; Liu, H.; Zhu, J.; Su, J.; and Zhang, R. In 2009 Sixth International Conference on Fuzzy Systems and Knowledge Discovery, volume 5, pages 33–36, 2009. IEEE
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HHMD: the human histone modification database. Zhang*✉, Y.; Lv*, J.; Liu*, H.; Zhu, J.; Su, J.; Wu, Q.; Qi, Y.; Wang, F.; and Li✉, X. Nucleic acids research, 38(suppl_1): D149–D154. 2009.
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CpG_MI: a novel approach for identifying functional CpG islands in mammalian genomes. Su*, J.; Zhang*✉, Y.; Lv*, J.; Liu, H.; Tang, X.; Wang, F.; Qi, Y.; Feng, Y.; and Li✉, X. Nucleic acids research, 38(1): e6–e6. 2009.
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