World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
37
Citations
6849
World Ranking
10620
National Ranking
159

Makoto Miwa 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 Makoto Miwa 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: 109 publications — 11th percentile

11% of scientists in this discipline score the same or lower.

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

Makoto Miwa 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 Makoto Miwa 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: 37 D-Index — 27th percentile

27% of scientists in this discipline score the same or lower.

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

Overview

Makoto Miwa is affiliated with the Toyota Technological Institute in Japan, contributing extensively to the field of computer science with a focus on artificial intelligence and related subfields.

Their publication record includes significant work in areas such as biomedical text mining, topic modeling, natural language processing techniques, and computational methods for drug discovery. They have also worked on machine learning applications in materials science and semantic web ontologies, as well as text readability and simplification.

Frequent research topics covered by Makoto Miwa include:

  • Topic Modeling
  • Biomedical Text Mining and Ontologies
  • Natural Language Processing Techniques
  • Machine Learning in Materials Science
  • Computational Drug Discovery Methods
  • Semantic Web and Ontologies
  • Text Readability and Simplification

Makoto Miwa's research spans several interconnected fields with the following breakdown in publication volume:

  • Artificial Intelligence (53 publications)
  • Molecular Biology (16 publications)
  • Materials Chemistry (10 publications)
  • Computational Theory and Mathematics (4 publications)
  • Computer Vision and Pattern Recognition (4 publications)

They have published frequently in these venues:

  • arXiv (Cornell University)
  • Bioinformatics
  • Zenodo (CERN European Organization for Nuclear Research)
  • Journal of Biomedical Informatics
  • IEEE Access

Some of their recent papers illustrate the breadth of their work in biomedical and computational text mining:

  • "Using drug descriptions and molecular structures for drug-drug interaction extraction from literature," 2020, Bioinformatics
  • "DeepEventMine: end-to-end neural nested event extraction from biomedical texts," 2020, Bioinformatics
  • "Integrating heterogeneous knowledge graphs into drug-drug interaction extraction from the literature," 2022, Bioinformatics
  • "Annotating and Extracting Synthesis Process of All-Solid-State Batteries from Scientific Literature," 2020, arXiv (Cornell University)
  • "Syntactically-informed word representations from graph neural network," 2020, Neurocomputing

Makoto Miwa frequently collaborates with a set of coauthors, including:

  • Sophia Ananiadou
  • Yutaka Sasaki
  • Kohei Makino
  • Fusataka Kuniyoshi
  • Jun Ozawa

Best Publications

  • End-to-End Relation Extraction using LSTMs on Sequences and Tree Structures

    Makoto Miwa;Mohit Bansal

  • Using text mining for study identification in systematic reviews: a systematic review of current approaches

    Alison O’Mara-Eves;James Thomas;John McNaught;Makoto Miwa

  • Modeling Joint Entity and Relation Extraction with Table Representation

    Makoto Miwa;Yutaka Sasaki

  • A Neural Layered Model for Nested Named Entity Recognition

    Meizhi Ju;Makoto Miwa;Sophia Ananiadou

  • Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs

    Fenia Christopoulou;Makoto Miwa;Makoto Miwa;Sophia Ananiadou

  • EVENT EXTRACTION WITH COMPLEX EVENT CLASSIFICATION USING RICH FEATURES

    Makoto Miwa;Rune Sætre;Jin-Dong Kim;Jun'ichi Tsujii;Jun'ichi Tsujii

  • Reducing systematic review workload through certainty-based screening

    Makoto Miwa;James Thomas;Alison O'Mara-Eves;Sophia Ananiadou

  • Deep Exhaustive Model for Nested Named Entity Recognition

    Mohammad Golam Sohrab;Makoto Miwa

  • Protein–protein interaction extraction by leveraging multiple kernels and parsers

    Makoto Miwa;Rune Sætre;Yusuke Miyao;Jun’ichi Tsujii;Jun’ichi Tsujii

  • Inter-sentence Relation Extraction with Document-level Graph Convolutional Neural Network

    Sunil Kumar Sahu;Fenia Christopoulou;Makoto Miwa;Sophia Ananiadou

  • Event extraction across multiple levels of biological organization

    Sampo Pyysalo;Tomoko Ohta;Makoto Miwa;Han-Cheol Cho

  • Discovering and visualizing indirect associations between biomedical concepts

    Yoshimasa Tsuruoka;Makoto Miwa;Kaisei Hamamoto;Jun'ichi Tsujii

  • Boosting automatic event extraction from the literature using domain adaptation and coreference resolution

    Makoto Miwa;Paul Thompson;Sophia Ananiadou

  • Simple Customization of Recursive Neural Networks for Semantic Relation Classification

    Kazuma Hashimoto;Makoto Miwa;Yoshimasa Tsuruoka;Takashi Chikayama

  • A Rich Feature Vector for Protein-Protein Interaction Extraction from Multiple Corpora

    Makoto Miwa;Rune Saetre;Yusuke Miyao;Jun'ichi Tsujii

  • Word Embedding-based Antonym Detection using Thesauri and Distributional Information

    Masataka Ono;Makoto Miwa;Yutaka Sasaki

  • Topic detection using paragraph vectors to support active learning in systematic reviews

    Kazuma Hashimoto;Georgios Kontonatsios;Makoto Miwa;Sophia Ananiadou

  • A Walk-based Model on Entity Graphs for Relation Extraction.

    Fenia Christopoulou;Makoto Miwa;Sophia Ananiadou

  • Adverse drug events and medication relation extraction in electronic health records with ensemble deep learning methods.

    Fenia Christopoulou;Thy Thy Tran;Thy Thy Tran;Sunil Kumar Sahu;Makoto Miwa

  • Extracting semantically enriched events from biomedical literature.

    Makoto Miwa;Paul Thompson;John McNaught;Douglas B Kell

Frequent Co-Authors

Sophia Ananiadou
Sophia Ananiadou University of Manchester
Jun'ichi Tsujii
Jun'ichi Tsujii University of Manchester
Sampo Pyysalo
Sampo Pyysalo University of Turku
Yoshimasa Tsuruoka
Yoshimasa Tsuruoka University of Tokyo
Tomoko Ohta
Tomoko Ohta University of Tokyo
Yusuke Miyao
Yusuke Miyao University of Tokyo
Mohit Bansal
Mohit Bansal University of North Carolina at Chapel Hill
Tapio Salakoski
Tapio Salakoski University of Turku
James Thomas
James Thomas University College London
Douglas B. Kell
Douglas B. Kell University of Liverpool

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