World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
65
Citations
16361
World Ranking
2463
National Ranking
1235

Jianlin Cheng 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 Jianlin Cheng 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: 249 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: 559 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: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 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: 227 publications — 56th percentile

56% of scientists in this discipline score the same or lower.

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

Jianlin Cheng 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 Jianlin Cheng sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 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: 65 D-Index — 83rd percentile

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

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

Overview

Jianlin Cheng is affiliated with the University of Missouri in the United States and specializes in research primarily within Biochemistry, Genetics, and Molecular Biology. Their scholarly work encompasses a significant volume of publications, with 364 in the main field and a detailed focus on subfields such as Molecular Biology, Materials Chemistry, Computational Theory and Mathematics, Plant Science, and Structural Biology.

Their research concentrates on several core topics, including:

  • Protein Structure and Dynamics
  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods
  • Enzyme Structure and Function
  • Advanced Electron Microscopy Techniques and Applications
  • Machine Learning in Materials Science
  • RNA and protein synthesis mechanisms

Jianlin Cheng has contributed to numerous papers across leading scientific venues. Recent notable publications include:

  • "Critical assessment of protein intrinsic disorder prediction," 2021, published in Nature Methods
  • "Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis," 2020, published in International Journal of Molecular Sciences
  • "Efficient Acceleration of Deep Learning Inference on Resource-Constrained Edge Devices: A Review," 2022, published in Proceedings of the IEEE
  • "Artificial intelligence in the prediction of protein-ligand interactions: recent advances and future directions," 2021, published in Briefings in Bioinformatics
  • "Prediction of protein assemblies, the next frontier: The CASP14-CAPRI experiment," 2021, published in Proteins Structure Function and Bioinformatics

The frequent co-authors working alongside Jianlin Cheng are:

  • Alex Morehead
  • Chen Chen
  • Zhiye Guo
  • Jie Hou
  • Tianqi Wu

Their research has appeared extensively in several scientific outlets, with the highest number of publications in:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Bioinformatics
  • arXiv (Cornell University)
  • Proteins Structure Function and Bioinformatics

Best Publications

  • Prediction of protein stability changes for single-site mutations using support vector machines.

    Jianlin Cheng;Arlo Randall;Pierre Baldi

  • SCRATCH: a protein structure and structural feature prediction server

    Jianlin Cheng;Arlo Z. Randall;Michael J. Sweredoski;Pierre Baldi

  • A large-scale evaluation of computational protein function prediction

    Predrag Radivojac;Wyatt T Clark;Tal Ronnen Oron;Alexandra M Schnoes

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

  • The CAFA challenge reports improved protein function prediction and new functional annotations for hundreds of genes through experimental screens

    Naihui Zhou;Yuxiang Jiang;Timothy R. Bergquist;Alexandra J. Lee

  • 3Drefine: an interactive web server for efficient protein structure refinement

    Debswapna Bhattacharya;Jackson Nowotny;Renzhi Cao;Jianlin Cheng

  • A deep learning network approach to ab initio protein secondary structure prediction

    Matt Spencer;Jesse Eickholt;Jianlin Cheng

  • Improved residue contact prediction using support vector machines and a large feature set

    Jianlin Cheng;Pierre Baldi

  • An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T Clark;Asma R Bankapur

  • Artificial intelligence in the prediction of protein–ligand interactions: recent advances and future directions

    Unknown

  • A machine learning information retrieval approach to protein fold recognition

    Jianlin Cheng;Pierre Baldi

  • Accurate Prediction of Protein Disordered Regions by Mining Protein Structure Data

    Jianlin Cheng;Michael J. Sweredoski;Pierre Baldi

  • Bioinformatics Methods for Mass Spectrometry-Based Proteomics Data Analysis.

    Chen Chen;Jie Hou;John J. Tanner;Jianlin Cheng

  • 3Drefine: consistent protein structure refinement by optimizing hydrogen bonding network and atomic-level energy minimization.

    Debswapna Bhattacharya;Jianlin Cheng

  • A neural network approach to ordinal regression

    Jianlin Cheng;Zheng Wang;G. Pollastri

  • DeepSF: Deep Convolutional Neural Network for Mapping Protein Sequences to Folds

    Jie Hou;Badri Adhikari;Jianlin Cheng

  • DeepQA: improving the estimation of single protein model quality with deep belief networks.

    Renzhi Cao;Debswapna Bhattacharya;Jie Hou;Jianlin Cheng

  • NNcon: improved protein contact map prediction using 2D-recursive neural networks

    Allison N. Tegge;Zheng Wang;Jesse Eickholt;Jianlin Cheng

  • Predicting protein residue–residue contacts using deep networks and boosting

    Jesse Eickholt;Jianlin Cheng

  • DNCON2: improved protein contact prediction using two-level deep convolutional neural networks.

    Badri Adhikari;Jie Hou;Jianlin Cheng;Jianlin Cheng

  • A neural network approach to ordinal regression

    Jianlin Cheng

  • Additional file 1 of An expanded evaluation of protein function prediction methods shows an improvement in accuracy

    Yuxiang Jiang;Tal Ronnen Oron;Wyatt T. Clark;Asma R. Bankapur

Frequent Co-Authors

Pierre Baldi
Pierre Baldi University of California, Irvine
Gary Stacey
Gary Stacey University of Missouri
James A. Birchler
James A. Birchler University of Missouri
Dong Xu
Dong Xu University of Missouri
Daisuke Kihara
Daisuke Kihara Purdue University West Lafayette
Christophe Dessimoz
Christophe Dessimoz University College London
Grace Y. Sun
Grace Y. Sun University of Missouri
Tapio Salakoski
Tapio Salakoski University of Turku
David T. Jones
David T. Jones University College London
Trupti Joshi
Trupti Joshi University of Missouri

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