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Yasubumi Sakakibara

Yasubumi Sakakibara

D-Index & Metrics

Engineering and Technology

D-Index
34
Citations
5765
World Ranking
9173
National Ranking
204

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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