World's Best Scientists 2026 revealed!
Jiangning Song

Jiangning Song

D-Index & Metrics

Computer Science

D-Index
59
Citations
10958
World Ranking
3488
National Ranking
102

Biology and Biochemistry

D-Index
61
Citations
12249
World Ranking
11435
National Ranking
310

Jiangning Song publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Jiangning Song sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 250 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 560 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 424 scientists 242–251 publications: 408 scientists 252–261 publications: 378 scientists 262–271 publications: 300 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 192 publications — 43rd percentile

43% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Jiangning Song D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Jiangning Song sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 984 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 969 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 765 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 517 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Jiangning Song is affiliated with Monash University in Australia and has contributed extensively to the field of biochemistry, genetics, and molecular biology. Their research portfolio encompasses a wide range of topics with a strong focus on machine learning applications in bioinformatics.

The main topics of Jiangning Song's work include:

  • Machine Learning in Bioinformatics
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Vaccines and immunoinformatics approaches
  • Bioinformatics and Genomic Networks
  • AI in cancer detection
  • Air Quality and Health Impacts

Their research spans various subfields such as molecular biology, health, toxicology and mutagenesis, artificial intelligence, cancer research, and radiology, nuclear medicine and imaging. Jiangning Song's publication record reflects an emphasis on both foundational molecular studies and cutting-edge AI-based methodologies.

Jiangning Song has coauthored frequently with:

  • Fuyi Li
  • Dong-Jun Yu
  • Zhikang Wang
  • Yuming Guo
  • Shanshan Li

The scientist publishes predominantly in the following venues:

  • Briefings in Bioinformatics
  • bioRxiv (Cold Spring Harbor Laboratory)
  • Bioinformatics
  • Journal of Chemical Information and Modeling
  • SSRN Electronic Journal

Recent papers highlight the focus and interdisciplinary nature of their research. Notable publications include:

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization, 2021, Nucleic Acids Research
  • Feature Erasing and Diffusion Network for Occluded Person Re-Identification, 2022, 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
  • Global estimates of daily ambient fine particulate matter concentrations and unequal spatiotemporal distribution of population exposure: a machine learning modelling study, 2023, The Lancet Planetary Health
  • An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP, 2020, Molecular Therapy - Nucleic Acids
  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides, 2021, Briefings in Bioinformatics

Best Publications

  • iFeature: a Python package and web server for features extraction and selection from protein and peptide sequences.

    Zhen Chen;Pei Zhao;Fuyi Li;André Leier

  • ACPred-FL: a sequence-based predictor using effective feature representation to improve the prediction of anti-cancer peptides.

    Leyi Wei;Chen Zhou;Huangrong Chen;Jiangning Song

  • iLearn: an integrated platform and meta-learner for feature engineering, machine-learning analysis and modeling of DNA, RNA and protein sequence data.

    Zhen Chen;Pei Zhao;Fuyi Li;Tatiana T Marquez-Lago

  • PROSPER: An Integrated Feature-Based Tool for Predicting Protease Substrate Cleavage Sites

    Jiangning Song;Jiangning Song;Hao Tan;Andrew J. Perry;Tatsuya Akutsu

  • iLearnPlus: a comprehensive and automated machine-learning platform for nucleic acid and protein sequence analysis, prediction and visualization.

    Zhen Chen;Pei Zhao;Chen Li;Fuyi Li;Fuyi Li

  • APIS: accurate prediction of hot spots in protein interfaces by combining protrusion index with solvent accessibility

    Jun Feng Xia;Jun Feng Xia;Xing Ming Zhao;Jiangning Song;Jiangning Song;De Shuang Huang

  • iProt-Sub: a comprehensive package for accurately mapping and predicting protease-specific substrates and cleavage sites

    Jiangning Song;Yanan Wang;Fuyi Li;Tatsuya Akutsu

  • POSSUM: a bioinformatics toolkit for generating numerical sequence feature descriptors based on PSSM profiles.

    Jiawei Wang;Bingjiao Yang;Jerico Nico De Leon Revote;André Leier

  • GlycoMine: a machine learning-based approach for predicting N-, C- and O-linked glycosylation in the human proteome

    Fuyi Li;Chen Li;Mingjun Wang;Geoffrey I. Webb

  • Cascleave: towards more accurate prediction of caspase substrate cleavage sites.

    Jiangning Song;Hao Tan;Hongbin Shen;Khalid Mahmood

  • Quokka: a comprehensive tool for rapid and accurate prediction of kinase family-specific phosphorylation sites in the human proteome.

    Fuyi Li;Chen Li;Chen Li;Tatiana T Marquez-Lago;André Leier

  • A comprehensive review and performance evaluation of bioinformatics tools for HLA class I peptide-binding prediction

    Shutao Mei;Fuyi Li;André Leier;Tatiana T Marquez-Lago

  • A subset of HLA-I peptides are not genomically templated: Evidence for cis- and trans-spliced peptide ligands.

    Pouya Faridi;Chen Li;Chen Li;Sri H. Ramarathinam;Julian P. Vivian;Julian P. Vivian

  • PROSPERous: high-throughput prediction of substrate cleavage sites for 90 proteases with improved accuracy.

    Jiangning Song;Fuyi Li;Andre Leier;Tatiana Marquez-Lago

  • hCKSAAP_UbSite: Improved prediction of human ubiquitination sites by exploiting amino acid pattern and properties

    Zhen Chen;Yuan Zhou;Jiangning Song;Jiangning Song;Ziding Zhang

  • Computational enzyme design approaches with significant biological outcomes: progress and challenges

    Xiaoman Li;Ziding Zhang;Jiangning Song

  • PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural, and network features in a machine-learning framework.

    Jiangning Song;Fuyi Li;Kazuhiro Takemoto;Gholamreza Haffari

  • An Interpretable Prediction Model for Identifying N7-Methylguanosine Sites Based on XGBoost and SHAP.

    Yue Bi;Dongxu Xiang;Zongyuan Ge;Fuyi Li

  • Production of octenyl succinic anhydride-modified waxy corn starch and its characterization.

    Zhiqiang Liu;Yin Li;Fengjie Cui;Lifeng Ping

  • Bastion6: a bioinformatics approach for accurate prediction of type VI secreted effectors.

    Jiawei Wang;Bingjiao Yang;André Leier;Tatiana T Marquez-Lago

  • Comprehensive assessment of machine learning-based methods for predicting antimicrobial peptides.

    Jing Xu;Fuyi Li;André Leier;Dongxu Xiang

  • Prediction of protein folding rates from primary sequence by fusing multiple sequential features

    Hong-Bin Shen;Jiang-Ning Song;Kuo-Chen Chou

  • Fabrication of high-Q lithium niobate microresonators using femtosecond laser micromachining for second harmonic generation

    J. Lin;Y. Xu;Z. Fang;M. Wang

Frequent Co-Authors

Tatsuya Akutsu
Tatsuya Akutsu Kyoto University
Geoffrey I. Webb
Geoffrey I. Webb Monash University
André Leier
André Leier University of Alabama at Birmingham
Kuo-Chen Chou
Kuo-Chen Chou The Gordon Life Science Institute
Trevor Lithgow
Trevor Lithgow Monash University
Anthony W. Purcell
Anthony W. Purcell Monash University
James C. Whisstock
James C. Whisstock Monash University
Roger J. Daly
Roger J. Daly Monash University
Hong-Bin Shen
Hong-Bin Shen Shanghai Jiao Tong University
Yanhe Ma
Yanhe Ma Chinese Academy of Sciences

If you think any of the details on this page are incorrect, let us know.

Report an issue

We appreciate your kind effort to assist us to improve this page, it would be helpful providing us with as much detail as possible in the text box below:

Related Online Degrees & Career Pathways

Exploring a career in Computer Science opens doors to a range of educational pathways. Many students start with an associate degree in 6 months online, which can provide foundational knowledge quickly and cost-effectively. This option is ideal for those seeking to enter the workforce early or transfer to a four-year program.

For those looking to advance their expertise, pursuing the cheapest online masters programs in computer science can be a budget-friendly way to boost skills and career prospects. These flexible online options make it easier to balance studies with work or personal commitments.

Beyond a master's, doctorate programs offer leadership or academic pathways. If organizational leadership interests you, consider the cheapest online doctorate in organizational leadership. Education professionals might seek the cheapest doctorate of education online for advancement in academic settings.

Choosing the right online degree can help you build a specialized skill set and pursue diverse roles, from IT management to educational leadership.

Best Scientists Citing Jiangning Song

Trending Scientists

Recently Published Articles