World's Best Scientists 2026 revealed!

D-Index & Metrics

Genetics

D-Index
72
Citations
29183
World Ranking
2092
National Ranking
53

Liang-Hu Qu publication distribution in Genetics in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Genetics in 2026. The highlighted bar marks where Liang-Hu Qu sits on this spectrum.

45–54 publications: 6 scientists 55–64 publications: 10 scientists 65–74 publications: 35 scientists 75–84 publications: 84 scientists 85–94 publications: 102 scientists 95–104 publications: 151 scientists 105–114 publications: 175 scientists 115–124 publications: 203 scientists 125–134 publications: 217 scientists 135–144 publications: 205 scientists 145–154 publications: 193 scientists 155–164 publications: 188 scientists 165–174 publications: 170 scientists 175–184 publications: 178 scientists 185–194 publications: 164 scientists 195–204 publications: 173 scientists 205–214 publications: 159 scientists 215–224 publications: 134 scientists 225–234 publications: 143 scientists 235–244 publications: 105 scientists 245–254 publications: 114 scientists 255–264 publications: 92 scientists 265–274 publications: 88 scientists 275–284 publications: 87 scientists 285–294 publications: 80 scientists 295–304 publications: 62 scientists 305–314 publications: 75 scientists 315–324 publications: 67 scientists 325–334 publications: 60 scientists 335–344 publications: 52 scientists 345–354 publications: 40 scientists 355–364 publications: 48 scientists 365–374 publications: 47 scientists 375–384 publications: 46 scientists 385–394 publications: 31 scientists 395–404 publications: 27 scientists 405–414 publications: 40 scientists 415–424 publications: 30 scientists 425–434 publications: 43 scientists 435–444 publications: 29 scientists 445–454 publications: 14 scientists 455–464 publications: 28 scientists 465–474 publications: 21 scientists 475–484 publications: 21 scientists 485–494 publications: 22 scientists 495–504 publications: 17 scientists 505–514 publications: 12 scientists 515–524 publications: 11 scientists 525–534 publications: 8 scientists 535–544 publications: 8 scientists 545–554 publications: 14 scientists 555–564 publications: 4 scientists 565–574 publications: 11 scientists 575–584 publications: 5 scientists 585–594 publications: 11 scientists 595–604 publications: 12 scientists 605–614 publications: 7 scientists 615–624 publications: 6 scientists 625–634 publications: 10 scientists 635–644 publications: 9 scientists 645–654 publications: 10 scientists 655–664 publications: 6 scientists 665–674 publications: 6 scientists 675–684 publications: 6 scientists 685–694 publications: 4 scientists 695–702 publications: 6 scientists 703+ publications: 100 scientists
45 publications 703+

This scientist: 200 publications — 51st percentile

51% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 703 publications or more.

Liang-Hu Qu D-index placement in Genetics in 2026

The chart shows the D-index (discipline H-index) distribution of Genetics scientists ranked by Research.com in 2026. The highlighted bar marks where Liang-Hu Qu sits on this spectrum.

40–41 D-Index: 24 scientists 42–43 D-Index: 52 scientists 44–45 D-Index: 84 scientists 46–47 D-Index: 112 scientists 48–49 D-Index: 118 scientists 50–51 D-Index: 141 scientists 52–53 D-Index: 143 scientists 54–55 D-Index: 145 scientists 56–57 D-Index: 179 scientists 58–59 D-Index: 162 scientists 60–61 D-Index: 175 scientists 62–63 D-Index: 191 scientists 64–65 D-Index: 172 scientists 66–67 D-Index: 184 scientists 68–69 D-Index: 164 scientists 70–71 D-Index: 158 scientists 72–73 D-Index: 150 scientists 74–75 D-Index: 136 scientists 76–77 D-Index: 127 scientists 78–79 D-Index: 127 scientists 80–81 D-Index: 111 scientists 82–83 D-Index: 110 scientists 84–85 D-Index: 110 scientists 86–87 D-Index: 84 scientists 88–89 D-Index: 102 scientists 90–91 D-Index: 66 scientists 92–93 D-Index: 72 scientists 94–95 D-Index: 70 scientists 96–97 D-Index: 54 scientists 98–99 D-Index: 60 scientists 100–101 D-Index: 49 scientists 102–103 D-Index: 55 scientists 104–105 D-Index: 45 scientists 106–107 D-Index: 42 scientists 108–109 D-Index: 28 scientists 110–111 D-Index: 39 scientists 112–113 D-Index: 25 scientists 114–115 D-Index: 31 scientists 116–117 D-Index: 29 scientists 118–119 D-Index: 34 scientists 120–121 D-Index: 29 scientists 122–123 D-Index: 29 scientists 124–125 D-Index: 18 scientists 126–127 D-Index: 27 scientists 128–129 D-Index: 22 scientists 130–131 D-Index: 16 scientists 132–133 D-Index: 11 scientists 134–135 D-Index: 17 scientists 136–137 D-Index: 12 scientists 138–139 D-Index: 21 scientists 140–141 D-Index: 4 scientists 142–143 D-Index: 9 scientists 144–145 D-Index: 14 scientists 146–147 D-Index: 6 scientists 148–149 D-Index: 10 scientists 150–151 D-Index: 7 scientists 152–153 D-Index: 9 scientists 154–155 D-Index: 8 scientists 156–157 D-Index: 8 scientists 158–159 D-Index: 9 scientists 160+ D-Index: 96 scientists
40 D-Index 160+

This scientist: 72 D-Index — 52nd percentile

52% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 160 D-Index or more.

Overview

Liang-Hu Qu is a researcher affiliated with Sun Yat-sen University in China, focusing primarily on biochemistry, genetics, and molecular biology. Their scholarly output includes 85 publications predominantly within the field of molecular biology, covering related subfields such as cancer research, cell biology, oncology, and physiology. The research topics addressed span RNA modifications and cancer, RNA research and splicing, cancer-related molecular mechanisms, RNA and protein synthesis mechanisms, CRISPR and genetic engineering, microRNA in disease regulation, and PI3K/AKT/mTOR signaling in cancer.

Frequent coauthors collaborating with Liang-Hu Qu include Jianhua Yang, Bin Li, Ling-Ling Zheng, Shurong Liu, and Qiao-Juan Huang. The scientist consistently publishes in several key venues, most notably Nucleic Acids Research, where eight papers appear, alongside Science China Life Sciences, Advanced Biotechnology, Accounts of Chemical Research, and Molecular Therapy.

Recent significant publications illustrate the scope and focus of their work:

  • RMBase v3.0: decode the landscape, mechanisms and functions of RNA modifications (2023, Nucleic Acids Research)
  • tsRFun: a comprehensive platform for decoding human tsRNA expression, functions and prognostic value by high-throughput small RNA-Seq and CLIP-Seq data (2021, Nucleic Acids Research)
  • The cardiac translational landscape reveals that micropeptides are new players involved in cardiomyocyte hypertrophy (2021, Molecular Therapy)
  • Classification and function of RNA-protein interactions (2020, Wiley Interdisciplinary Reviews - RNA)
  • deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data (2020, Nucleic Acids Research)

Their publications contribute to understanding RNA modifications and their implications in cancer biology and RNA-protein interactions. The work involves developing computational platforms and expression atlases, supporting in-depth investigation of RNA molecules and the molecular mechanisms underlying various diseases.

Best Publications

  • starBase v2.0: decoding miRNA-ceRNA, miRNA-ncRNA and protein–RNA interaction networks from large-scale CLIP-Seq data

    Jun Hao Li;Shun Liu;Hui Zhou;Liang Hu Qu

  • The Chlamydomonas Genome Reveals the Evolution of Key Animal and Plant Functions

    Sabeeha S. Merchant;Simon E. Prochnik;Olivier Vallon;Elizabeth H. Harris

  • Recognition of RNA N 6 -methyladenosine by IGF2BP proteins enhances mRNA stability and translation

    Huilin Huang;Huilin Huang;Hengyou Weng;Hengyou Weng;Wenju Sun;Xi Qin;Xi Qin

  • starBase: a database for exploring microRNA–mRNA interaction maps from Argonaute CLIP-Seq and Degradome-Seq data

    Jian-Hua Yang;Jun-Hao Li;Peng Shao;Hui Zhou

  • Histone H3 trimethylation at lysine 36 guides m 6 A RNA modification co-transcriptionally

    Huilin Huang;Huilin Huang;Hengyou Weng;Hengyou Weng;Keren Zhou;Tong Wu

  • Genome-wide screening and functional analysis identify a large number of long noncoding RNAs involved in the sexual reproduction of rice

    Yu-Chan Zhang;Jian-You Liao;Ze-Yuan Li;Yang Yu

  • Overexpression of microRNA OsmiR397 improves rice yield by increasing grain size and promoting panicle branching

    Yu-Chan Zhang;Yang Yu;Cong-Ying Wang;Ze-Yuan Li

  • DNA-binding and cleavage studies of macrocyclic copper(II) complexes

    Jie Liu;Tixiang Zhang;Tongbu Lu;Lianghu Qu

  • Pachytene piRNAs instruct massive mRNA elimination during late spermiogenesis

    Lan Tao Gou;Peng Dai;Jian Hua Yang;Yuanchao Xue

  • ChIPBase: a database for decoding the transcriptional regulation of long non-coding RNA and microRNA genes from ChIP-Seq data

    Jian-Hua Yang;Jun-Hao Li;Shan Jiang;Hui Zhou

  • RMBase v2.0: deciphering the map of RNA modifications from epitranscriptome sequencing data

    Jia-Jia Xuan;Wen-Ju Sun;Peng-Hui Lin;Ke-Ren Zhou

  • Deep sequencing of human nuclear and cytoplasmic small RNAs reveals an unexpectedly complex subcellular distribution of miRNAs and tRNA 3' trailers.

    Jian-You Liao;Li-Ming Ma;Yan-Hua Guo;Yu-Chan Zhang

  • Liver‐enriched transcription factors regulate MicroRNA‐122 that targets CUTL1 during liver development

    Hui Xu;Jie Hua He;Zhen Dong Xiao;Qian Qian Zhang

  • Phylogeny and biogeography of the family Salamandridae (Amphibia: Caudata) inferred from complete mitochondrial genomes

    Peng Zhang;Theodore J. Papenfuss;Marvalee H. Wake;Lianghu Qu

  • MicroRNA-21 promotes cell proliferation and down-regulates the expression of programmed cell death 4 (PDCD4) in HeLa cervical carcinoma cells.

    Qing Yao;Hui Xu;Qian-Qian Zhang;Hui Zhou

  • ChIPBase v2.0: decoding transcriptional regulatory networks of non-coding RNAs and protein-coding genes from ChIP-seq data

    Ke-Ren Zhou;Shun Liu;Wen-Ju Sun;Ling-Ling Zheng

  • Expression analysis of phytohormone-regulated microRNAs in rice, implying their regulation roles in plant hormone signaling.

    Qing Liu;Yu-Chan Zhang;Cong-Ying Wang;Yu-Chun Luo

  • MicroRNA patterns associated with clinical prognostic parameters and CNS relapse prediction in pediatric acute leukemia.

    Hua Zhang;Xue-Qun Luo;Peng Zhang;Li-Bin Huang

  • Down-regulated miR-331-5p and miR-27a are associated with chemotherapy resistance and relapse in leukaemia.

    Dan-Dan Feng;Hua Zhang;Peng Zhang;Yu-Sheng Zheng

  • Identification of 20 microRNAs from Oryza sativa

    Jia‐Fu Wang;Hui Zhou;Yue‐Qin Chen;Qing‐Jun Luo

Frequent Co-Authors

Hui Zhou
Hui Zhou Sun Yat-sen University
Yue-Qin Chen
Yue-Qin Chen Sun Yat-sen University
Jean-Pierre Bachellerie
Jean-Pierre Bachellerie Paul Sabatier University
Liang-Nian Ji
Liang-Nian Ji Sun Yat-sen University
Chuan He
Chuan He University of Chicago
Francisco J. Ayala
Francisco J. Ayala University of California, Irvine
Jun-Lin Guan
Jun-Lin Guan University of Cincinnati Medical Center
Michèle Caizergues-Ferrer
Michèle Caizergues-Ferrer Centre national de la recherche scientifique, CNRS
Yan Li
Yan Li Sun Yat-sen University
Da-Zhi Wang
Da-Zhi Wang Boston Children's Hospital

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