World's Best Scientists 2026 revealed!

D-Index & Metrics

Chemistry

D-Index
82
Citations
27046
World Ranking
3058
National Ranking
558

Yi-Zeng Liang publication distribution in Chemistry in 2026

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

61–80 publications: 66 scientists 81–100 publications: 302 scientists 101–120 publications: 623 scientists 121–140 publications: 918 scientists 141–160 publications: 1,218 scientists 161–180 publications: 1,350 scientists 181–200 publications: 1,344 scientists 201–220 publications: 1,281 scientists 221–240 publications: 1,216 scientists 241–260 publications: 1,100 scientists 261–280 publications: 979 scientists 281–300 publications: 939 scientists 301–320 publications: 764 scientists 321–340 publications: 643 scientists 341–360 publications: 628 scientists 361–380 publications: 522 scientists 381–400 publications: 459 scientists 401–420 publications: 397 scientists 421–440 publications: 327 scientists 441–460 publications: 270 scientists 461–480 publications: 265 scientists 481–500 publications: 252 scientists 501–520 publications: 201 scientists 521–540 publications: 185 scientists 541–560 publications: 148 scientists 561–580 publications: 148 scientists 581–600 publications: 132 scientists 601–620 publications: 114 scientists 621–640 publications: 104 scientists 641–660 publications: 91 scientists 661–680 publications: 92 scientists 681–700 publications: 73 scientists 701–720 publications: 57 scientists 721–740 publications: 54 scientists 741–760 publications: 67 scientists 761–780 publications: 45 scientists 781–800 publications: 46 scientists 801–820 publications: 39 scientists 821–840 publications: 32 scientists 841–860 publications: 36 scientists 861–880 publications: 29 scientists 881–900 publications: 26 scientists 901–920 publications: 24 scientists 921–940 publications: 14 scientists 941–960 publications: 23 scientists 961–980 publications: 28 scientists 981–1,000 publications: 15 scientists 1,001–1,020 publications: 29 scientists 1,021–1,040 publications: 12 scientists 1,041–1,060 publications: 19 scientists 1,061–1,080 publications: 12 scientists 1,081–1,100 publications: 6 scientists 1,101–1,120 publications: 8 scientists 1,121–1,140 publications: 12 scientists 1,141–1,160 publications: 5 scientists 1,161–1,180 publications: 6 scientists 1,181–1,200 publications: 14 scientists 1,201–1,220 publications: 7 scientists 1,221–1,240 publications: 2 scientists 1,241–1,260 publications: 6 scientists 1,261–1,280 publications: 4 scientists 1,281–1,294 publications: 6 scientists 1,295+ publications: 100 scientists
61 publications 1,295+

This scientist: 424 publications — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 1,295 publications or more.

Yi-Zeng Liang D-index placement in Chemistry in 2026

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

40–41 D-Index: 289 scientists 42–43 D-Index: 612 scientists 44–45 D-Index: 808 scientists 46–47 D-Index: 776 scientists 48–49 D-Index: 835 scientists 50–51 D-Index: 861 scientists 52–53 D-Index: 872 scientists 54–55 D-Index: 933 scientists 56–57 D-Index: 1,051 scientists 58–59 D-Index: 930 scientists 60–61 D-Index: 882 scientists 62–63 D-Index: 834 scientists 64–65 D-Index: 731 scientists 66–67 D-Index: 775 scientists 68–69 D-Index: 683 scientists 70–71 D-Index: 646 scientists 72–73 D-Index: 561 scientists 74–75 D-Index: 501 scientists 76–77 D-Index: 437 scientists 78–79 D-Index: 388 scientists 80–81 D-Index: 354 scientists 82–83 D-Index: 292 scientists 84–85 D-Index: 275 scientists 86–87 D-Index: 254 scientists 88–89 D-Index: 235 scientists 90–91 D-Index: 185 scientists 92–93 D-Index: 192 scientists 94–95 D-Index: 155 scientists 96–97 D-Index: 163 scientists 98–99 D-Index: 125 scientists 100–101 D-Index: 105 scientists 102–103 D-Index: 105 scientists 104–105 D-Index: 112 scientists 106–107 D-Index: 88 scientists 108–109 D-Index: 68 scientists 110–111 D-Index: 69 scientists 112–113 D-Index: 65 scientists 114–115 D-Index: 79 scientists 116–117 D-Index: 61 scientists 118–119 D-Index: 44 scientists 120–121 D-Index: 37 scientists 122–123 D-Index: 40 scientists 124–125 D-Index: 33 scientists 126–127 D-Index: 26 scientists 128–129 D-Index: 34 scientists 130–131 D-Index: 35 scientists 132–133 D-Index: 25 scientists 134–135 D-Index: 27 scientists 136–137 D-Index: 17 scientists 138–139 D-Index: 16 scientists 140–141 D-Index: 20 scientists 142–143 D-Index: 20 scientists 144–145 D-Index: 15 scientists 146–147 D-Index: 9 scientists 148–149 D-Index: 9 scientists 150–151 D-Index: 16 scientists 152–153 D-Index: 11 scientists 154–155 D-Index: 9 scientists 156–157 D-Index: 3 scientists 158 D-Index: 3 scientists 159+ D-Index: 98 scientists
40 D-Index 159+

This scientist: 82 D-Index — 83rd percentile

83% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 159 D-Index or more.

Overview

Yi-Zeng Liang is affiliated with Central South University in China and has contributed to research primarily within the field of Computer Science, with specific focus on Computational Theory and Mathematics. Their work also intersects with related subfields including Geometry and Topology, Analytical Chemistry, Cellular and Molecular Neuroscience, and Molecular Biology.

Their research topics cover a range of areas such as Computational Drug Discovery Methods, Graph Theory and Applications, Nuclear Receptors and Signaling, Spectroscopy and Chemometric Analyses, Metabolomics and Mass Spectrometry Studies, Data Mining Algorithms and Applications, and Face and Expression Recognition.

The scientist has published multiple papers, mainly in the Journal of Data Science, reflecting a consistent engagement with this venue. Notable recent papers include:

  • The Matrix Expression, Topological Index and Atomic Attribute of Molecular Topological Structure (2021, Journal of Data Science)
  • Data Mining in Chemometrics: Sub-structures Learning via Peak Combinations Searching in Mass Spectra (2021, Journal of Data Science)
  • Building an Honest Tree for Mass Spectra Classification Based on Prior Logarithm Normal Distribution (2021, Journal of Data Science)
  • Boiling Points Predictions Study via Dimension Reduction Methods: SIR, PCR and PLSR (2021, Journal of Data Science)
  • Application of Orthogonal Block Variables and Canonical Correlation Analysis in Modeling Pharmacological Activity of Alkaloids from Plant Medicines (2021, Journal of Data Science)

In addition to their own authorship, Yi-Zeng Liang has collaborated frequently with other researchers including Qiannan Hu, Kai-Tai Yi-Zeng, Yu Tang, Cheng-Jian Xu, and Ping He.

Their publication record also includes contributions to other venues such as PubMed and arXiv (Cornell University), though the majority of their output is concentrated in computational and data science journals.

Best Publications

  • Key wavelengths screening using competitive adaptive reweighted sampling method for multivariate calibration.

    Hongdong Li;Yizeng Liang;Qingsong Xu;Dongsheng Cao

  • Monte Carlo cross validation

    Qing-Song Xu;Yi-Zeng Liang

  • Baseline correction using adaptive iteratively reweighted penalized least squares

    Zhi-Min Zhang;Shan Chen;Yi-Zeng Liang

  • Quality control of herbal medicines

    Yi-Zeng Liang;Peishan Xie;Kelvin C. Chan

  • Heuristic evolving latent projections: resolving two-way multicomponent data. 1. Selectivity, latent-projective graph, datascope, local rank, and unique resolution

    Olav M. Kvalheim;Yi Zeng. Liang

  • Chromatographic fingerprint analysis--a rational approach for quality assessment of traditional Chinese herbal medicine.

    Peishan Xie;Sibao Chen;Yi-zeng Liang;Xianghong Wang

  • propy: a tool to generate various modes of Chou’s PseAAC

    Dong-Sheng Cao;Qing-Song Xu;Yi-Zeng Liang

  • Heuristic evolving latent projections: resolving two-way multicomponent data. 2. Detection and resolution of minor constituents

    Yi Zeng. Liang;Olav M. Kvalheim;Hans R. Keller;D. Luc. Massart

  • Support vector machines and its applications in chemistry

    Hongdong Li;Yizeng Liang;Qingsong Xu

  • An intelligent background-correction algorithm for highly fluorescent samples in Raman spectroscopy

    Zhi-Min Zhang;Shan Chen;Yi-Zeng Liang;Zhao-Xia Liu

  • Uniform design and its applications in chemistry and chemical engineering

    Yi-zeng Liang;Kai-tai Fang;Qing-song Xu

  • In vitro antioxidant and antimicrobial activities of the extract of Pericarpium Citri Reticulatae of a new Citrus cultivar and its main flavonoids

    Zhi Biao Yi;Yan Yu;Yi Zeng Liang;Bao Zeng

  • Information theory applied to chromatographic fingerprint of herbal medicine for quality control.

    Fan Gong;Yi-Zeng Liang;Pei-Shan Xie;Foo-Tim Chau

  • Spectral regions selection to improve prediction ability of PLS models by changeable size moving window partial least squares and searching combination moving window partial least squares

    Y.P. Du;Y.P. Du;Y.Z. Liang;J.H. Jiang;R.J. Berry

  • Random frog: An efficient reversible jump Markov Chain Monte Carlo-like approach for variable selection with applications to gene selection and disease classification

    Hong-Dong Li;Qing-Song Xu;Yi-Zeng Liang

  • Chemometric methods in data processing of mass spectrometry-based metabolomics: A review

    Lunzhao Yi;Naiping Dong;Yonghuan Yun;Baichuan Deng

  • libPLS: An integrated library for partial least squares regression and linear discriminant analysis

    Hong-Dong Li;Qing-Song Xu;Yi-Zeng Liang

  • Monte Carlo cross-validation for selecting a model and estimating the prediction error in multivariate calibration

    Qing-Song Xu;Yi-Zeng Liang;Yi-Ping Du

  • ChemoPy: freely available python package for computational biology and chemoinformatics

    Dong-Sheng Cao;Qing-Song Xu;Qian-Nan Hu;Yi-Zeng Liang

  • A strategy that iteratively retains informative variables for selecting optimal variable subset in multivariate calibration.

    Yong-Huan Yun;Wei-Ting Wang;Min-Li Tan;Yi-Zeng Liang

  • Development of high-performance liquid chromatographic fingerprints for distinguishing Chinese Angelica from related umbelliferae herbs.

    Guang-Hua Lu;Kelvin C. Chan;Yi-Zeng Liang;Kelvin Sze-Yin Leung

Frequent Co-Authors

Qing-Song Xu
Qing-Song Xu Central South University
Dong-Sheng Cao
Dong-Sheng Cao Central South University
Olav M. Kvalheim
Olav M. Kvalheim University of Bergen
Ru-Qin Yu
Ru-Qin Yu Hunan University
Kai-Tai Fang
Kai-Tai Fang Beijing Normal University
Jian-Hui Jiang
Jian-Hui Jiang Hunan University
Yukihiro Ozaki
Yukihiro Ozaki Kwansei Gakuin University
Desire L. Massart
Desire L. Massart Vrije Universiteit Brussel
Junbin Gao
Junbin Gao University of Sydney
Xiaoqing Chen
Xiaoqing Chen Central South University

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