World's Best Scientists 2026 revealed!
Yasubumi Sakakibara

Yasubumi Sakakibara

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
5765
World Ranking
9175
National Ranking
204

Yasubumi Sakakibara publication distribution in Engineering and Technology in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Engineering and Technology in 2026. The highlighted bar marks where Yasubumi Sakakibara sits on this spectrum.

38–47 publications: 20 scientists 48–57 publications: 35 scientists 58–67 publications: 96 scientists 68–77 publications: 135 scientists 78–87 publications: 190 scientists 88–97 publications: 259 scientists 98–107 publications: 283 scientists 108–117 publications: 369 scientists 118–127 publications: 341 scientists 128–137 publications: 386 scientists 138–147 publications: 372 scientists 148–157 publications: 457 scientists 158–167 publications: 415 scientists 168–177 publications: 407 scientists 178–187 publications: 421 scientists 188–197 publications: 378 scientists 198–207 publications: 403 scientists 208–217 publications: 317 scientists 218–227 publications: 346 scientists 228–237 publications: 321 scientists 238–247 publications: 260 scientists 248–257 publications: 280 scientists 258–267 publications: 240 scientists 268–277 publications: 214 scientists 278–287 publications: 242 scientists 288–297 publications: 203 scientists 298–307 publications: 166 scientists 308–317 publications: 154 scientists 318–327 publications: 175 scientists 328–337 publications: 159 scientists 338–347 publications: 99 scientists 348–357 publications: 131 scientists 358–367 publications: 106 scientists 368–377 publications: 118 scientists 378–387 publications: 97 scientists 388–397 publications: 108 scientists 398–407 publications: 82 scientists 408–417 publications: 71 scientists 418–427 publications: 64 scientists 428–437 publications: 55 scientists 438–447 publications: 54 scientists 448–457 publications: 60 scientists 458–467 publications: 47 scientists 468–477 publications: 40 scientists 478–487 publications: 30 scientists 488–497 publications: 29 scientists 498–507 publications: 38 scientists 508–517 publications: 40 scientists 518–527 publications: 32 scientists 528–537 publications: 23 scientists 538–547 publications: 28 scientists 548–557 publications: 23 scientists 558–567 publications: 19 scientists 568–577 publications: 16 scientists 578–587 publications: 17 scientists 588–597 publications: 18 scientists 598–607 publications: 22 scientists 608–617 publications: 15 scientists 618–627 publications: 9 scientists 628–637 publications: 11 scientists 638–647 publications: 21 scientists 648–657 publications: 12 scientists 658–667 publications: 9 scientists 668–677 publications: 11 scientists 678–687 publications: 9 scientists 688–697 publications: 6 scientists 698–707 publications: 14 scientists 708–717 publications: 7 scientists 718–727 publications: 8 scientists 728–737 publications: 10 scientists 738–747 publications: 9 scientists 748–757 publications: 5 scientists 758–767 publications: 5 scientists 768–777 publications: 11 scientists 778–787 publications: 7 scientists 788–797 publications: 2 scientists 798–803 publications: 4 scientists 804+ publications: 100 scientists
38 publications 804+

This scientist: 149 publications — 26th percentile

26% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 804 publications or more.

Yasubumi Sakakibara D-index placement in Engineering and Technology in 2026

The chart shows the D-index (discipline H-index) distribution of Engineering and Technology scientists ranked by Research.com in 2026. The highlighted bar marks where Yasubumi Sakakibara sits on this spectrum.

30 D-Index: 59 scientists 31 D-Index: 114 scientists 32 D-Index: 129 scientists 33 D-Index: 189 scientists 34 D-Index: 200 scientists 35 D-Index: 262 scientists 36 D-Index: 311 scientists 37 D-Index: 312 scientists 38 D-Index: 350 scientists 39 D-Index: 385 scientists 40 D-Index: 348 scientists 41 D-Index: 362 scientists 42 D-Index: 426 scientists 43 D-Index: 380 scientists 44 D-Index: 310 scientists 45 D-Index: 341 scientists 46 D-Index: 301 scientists 47 D-Index: 306 scientists 48 D-Index: 271 scientists 49 D-Index: 246 scientists 50 D-Index: 210 scientists 51 D-Index: 253 scientists 52 D-Index: 213 scientists 53 D-Index: 221 scientists 54 D-Index: 195 scientists 55 D-Index: 186 scientists 56 D-Index: 170 scientists 57 D-Index: 167 scientists 58 D-Index: 166 scientists 59 D-Index: 144 scientists 60 D-Index: 152 scientists 61 D-Index: 141 scientists 62 D-Index: 138 scientists 63 D-Index: 131 scientists 64 D-Index: 118 scientists 65 D-Index: 114 scientists 66 D-Index: 119 scientists 67 D-Index: 95 scientists 68 D-Index: 87 scientists 69 D-Index: 77 scientists 70 D-Index: 89 scientists 71 D-Index: 69 scientists 72 D-Index: 54 scientists 73 D-Index: 46 scientists 74 D-Index: 55 scientists 75 D-Index: 54 scientists 76 D-Index: 49 scientists 77 D-Index: 53 scientists 78 D-Index: 46 scientists 79 D-Index: 28 scientists 80 D-Index: 39 scientists 81 D-Index: 36 scientists 82 D-Index: 24 scientists 83 D-Index: 26 scientists 84 D-Index: 36 scientists 85 D-Index: 18 scientists 86 D-Index: 25 scientists 87 D-Index: 19 scientists 88 D-Index: 26 scientists 89 D-Index: 27 scientists 90 D-Index: 23 scientists 91 D-Index: 15 scientists 92 D-Index: 12 scientists 93 D-Index: 9 scientists 94 D-Index: 15 scientists 95 D-Index: 10 scientists 96 D-Index: 13 scientists 97 D-Index: 13 scientists 98 D-Index: 9 scientists 99 D-Index: 7 scientists 100 D-Index: 7 scientists 101 D-Index: 8 scientists 102 D-Index: 7 scientists 103 D-Index: 7 scientists 104 D-Index: 9 scientists 105 D-Index: 6 scientists 106 D-Index: 9 scientists 107+ D-Index: 99 scientists
30 D-Index 107+

This scientist: 34 D-Index — 7th percentile

7% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 107 D-Index or more.

Overview

Yasubumi Sakakibara is affiliated with Keio University in Japan and has contributed extensively to the field of Biochemistry, Genetics and Molecular Biology, with a particular focus on Molecular Biology and Artificial Intelligence. Their research spans interdisciplinary domains including Biological Psychiatry, Computational Theory and Mathematics, and Plant Science.

The main topics covered in their research involve:

  • Gut microbiota and health
  • RNA and protein synthesis mechanisms
  • Genomics and Phylogenetic Studies
  • Bioinformatics and Genomic Networks
  • Tryptophan and brain disorders
  • Machine Learning in Bioinformatics
  • Computational Drug Discovery Methods

Recent publications authored by or associated with Sakakibara include:

  • RNA secondary structure prediction using deep learning with thermodynamic integration, 2021, Nature Communications
  • Informative RNA base embedding for RNA structural alignment and clustering by deep representation learning, 2022, NAR Genomics and Bioinformatics
  • Variational autoencoder-based chemical latent space for large molecular structures with 3D complexity, 2023, Communications Chemistry
  • Hamster PIWI proteins bind to piRNAs with stage-specific size variations during oocyte maturation, 2021, Nucleic Acids Research
  • Performance of a deep learning-based identification system for esophageal cancer from CT images, 2021, Esophagus

Frequent co-authors collaborating with Sakakibara include:

  • Erika Sasaki
  • Atsushi Toyoda
  • Takashi Inoue
  • Sumitaka Hase
  • Mika Uehara

The scientist's work has been published repeatedly in several venues, notably:

  • bioRxiv (Cold Spring Harbor Laboratory)
  • Zenodo (CERN European Organization for Nuclear Research)
  • Communications Chemistry
  • Genes
  • mSystems

Best Publications

  • Stochastic context-free grammars for tRNA modeling.

    Sakakibara Y;Brown M;Hughey R;Mian Is

  • RNA secondary structure prediction using deep learning with thermodynamic integration

    Kengo Sato;Manato Akiyama;Yasubumi Sakakibara

  • Efficient learning of context-free grammars from positive structural examples

    Yasubumi Sakakibara

  • Learning context-free grammars from structural data in polynomial time

    Yasubumi Sakakibara

  • Recent advances of grammatical inference

    Yasubumi Sakakibara

  • Convolutional neural network based on SMILES representation of compounds for detecting chemical motif.

    Maya Hirohara;Yutaka Saito;Yuki Koda;Kengo Sato

  • Statistical prediction of protein–chemical interactions based on chemical structure and mass spectrometry data

    Nobuyoshi Nagamine;Yasubumi Sakakibara

  • Comprehensive evaluation of non-hybrid genome assembly tools for third-generation PacBio long-read sequence data.

    Vasanthan Jayakumar;Yasubumi Sakakibara

  • Chaperone Therapy for Neuronopathic Lysosomal Diseases: Competitive Inhibitors as Chemical Chaperones for Enhancement of Mutant Enzyme Activities:

    Yoshiyuki Suzuki;Seiichiro Ogawa;Yasubumi Sakakibara

  • MetaVelvet-SL: an extension of the Velvet assembler to a de novo metagenomic assembler utilizing supervised learning

    Afiahayati;Kengo Sato;Yasubumi Sakakibara

  • Pair stochastic tree adjoining grammars for aligning and predicting pseudoknot RNA structures

    Hiroshi Matsui;Kengo Sato;Yasubumi Sakakibara

  • Building of a document classification tree by recursive optimization of keyword selection function

    Yasubumi Sakakibara;Kazuo Misue

  • Grammatical inference in bioinformatics

    Y. Sakakibara

  • Convolutional neural networks for classification of alignments of non-coding RNA sequences.

    Genta Aoki;Yasubumi Sakakibara

  • DNA Computing and Molecular Programming

    Yasubumi Sakakibara;Yongli Mi

  • Pair hidden Markov models on tree structures.

    Yasubumi Sakakibara

  • Learning context-free grammars using tabular representations

    Yasubumi Sakakibara

  • Learning Context-Free Grammars from Partially Structured Examples

    Yasubumi Sakakibara;Hidenori Muramatsu

  • Identifying cooperative transcriptional regulations using protein-protein interactions.

    Nobuyoshi Nagamine;Yuji Kawada;Yasubumi Sakakibara

  • GA-based Learning of Context-Free Grammars using Tabular Representations

    Yasubumi Sakakibara;Mitsuhiro Kondo

  • On learning from queries and counterexamples in the presence of noise

    Yasubumi Sakakibara

  • DAFS: simultaneous aligning and folding of RNA sequences via dual decomposition.

    Kengo Sato;Yuki Kato;Tatsuya Akutsu;Kiyoshi Asai

  • Performance of a deep learning-based identification system for esophageal cancer from CT images.

    Masashi Takeuchi;Takumi Seto;Masahiro Hashimoto;Nao Ichihara

  • Recent Methods for RNA Modeling Using Stochastic Context-Free Grammars

    Yasubumi Sakakibara;Michael Brown;Richard Hughey;I. Saira Mian

  • Murasaki: A Fast, Parallelizable Algorithm to Find Anchors from Multiple Genomes

    Kris Popendorf;Hachiya Tsuyoshi;Yasunori Osana;Yasubumi Sakakibara

  • Erratum: Accurate identification of orthologous segments among multiple genomes (Bioinformatics (2009) vol. 25 (7) (853-860))

    Tsuyoshi Hachiya;Yasunori Osana;Kris Popendorf;Yasubumi Sakakibara

Frequent Co-Authors

Atsushi Toyoda
Atsushi Toyoda National Institute of Genetics
Asao Fujiyama
Asao Fujiyama National Institute of Genetics
Kiyoshi Asai
Kiyoshi Asai University of Tokyo
Hideyuki Okano
Hideyuki Okano Keio University
Masatsugu Ema
Masatsugu Ema Shiga University of Medical Science
David Haussler
David Haussler University of California, Santa Cruz
Takehiko Itoh
Takehiko Itoh Tokyo Institute of Technology
Shinichi Morishita
Shinichi Morishita University of Tokyo
Shigeru Iida
Shigeru Iida University of Shizuoka
Kazunori Nakajima
Kazunori Nakajima Keio University

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