World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
38
Citations
8827
World Ranking
10030
National Ranking
149

Overview

Takashi Masuko is affiliated with Preferred Networks, Inc. in Japan, contributing extensively to the fields of medicine and biochemistry, genetics, and molecular biology. Their research portfolio includes a focus on molecular biology and oncology, alongside radiology, nuclear medicine, and imaging.

Their work spans several subfields, including molecular biology, oncology, radiology, nuclear medicine and imaging, biochemistry, and organic chemistry. Masuko's research particularly explores monoclonal and polyclonal antibodies, HER2/EGFR pathways in cancer research, amino acid enzymes and metabolism, epigenetics and DNA methylation, glycosylation and glycoproteins, pancreatic function and diabetes, and advances in neuroendocrine tumor research.

Notable recent papers by Takashi Masuko include:

  • Selective targeting of multiple myeloma cells with a monoclonal antibody recognizing the ubiquitous protein CD98 heavy chain, 2022, Science Translational Medicine
  • Antitumor effects of novel mAbs against cationic amino acid transporter 1 (CAT1) on human CRC with amplified CAT1 gene, 2020, Cancer Science
  • HER4 and EGFR Activate Cell Signaling in NRG1 Fusion-Driven Cancers: Implications for HER2-HER3-specific Versus Pan-HER Targeting Strategies, 2023, Journal of Thoracic Oncology
  • Oncogenic transformation of NIH/3T3 cells by the overexpression of L-type amino acid transporter 1, a promising anti-cancer target, 2021, Oncotarget
  • Novel functional anti-HER3 monoclonal antibodies with potent anti-cancer effects on various human epithelial cancers, 2020, Oncotarget

Masuko frequently collaborates with several researchers, including Kazue Masuko, Yuichi Endo, Shogo Okazaki, Kouki Okita, and Yoshihisa Tomioka. These collaborations are reflected in recurring publications across a number of journals.

The primary publication venues for Masuko's research include Oncotarget, Genes to Cells, Science Translational Medicine, Cancer Science, and the Journal of Thoracic Oncology. Among these, Oncotarget and Genes to Cells represent the most frequent venues with multiple publications.

Best Publications

  • Speech parameter generation algorithms for HMM-based speech synthesis

    K. Tokuda;T. Yoshimura;T. Masuko;T. Kobayashi

  • Simultaneous Modeling of Spectrum, Pitch and Duration in HMM-Based Speech Synthesis

    Takayoshi Yoshimura;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

  • The HMM-based speech synthesis system (HTS) version 2.0.

    Heiga Zen;Takashi Nose;Junichi Yamagishi;Shinji Sako

  • Hidden Markov models based on multi-space probability distribution for pitch pattern modeling

    K. Tokuda;T. Masuko;N. Miyazaki;T. Kobayashi

  • Mel-generalized cepstral analysis - a unified approach to speech spectral estimation.

    Keiichi Tokuda;Takao Kobayashi;Takashi Masuko;Satoshi Imai

  • Multi-Space Probability Distribution HMM

    Keiichi Tokuda;Takashi Masuko;Noboru Miyazaki;Takao Kobayashi

  • A Hidden Semi-Markov Model-Based Speech Synthesis System

    Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayasih

  • Speech synthesis using HMMs with dynamic features

    T. Masuko;K. Tokuda;T. Kobayashi;S. Imai

  • Duration modeling for HMM-based speech synthesis.

    Takayoshi Yoshimura;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

  • Speaker Interpolation in HMM-Based Speech Synthesis System

    Takayoshi Yoshimura;Takashi Masuko;Keiichi Tokuda;Takao Kobayashi

  • Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR

    M. Tamura;T. Masuko;K. Tokuda;T. Kobayashi

  • Mixed Excitation for HMM-based Speech Synthesis

    Takayoshi Yoshimura;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

  • Hidden semi-Markov model based speech synthesis.

    Heiga Zen;Keiichi Tokuda;Takashi Masuko;Takao Kobayashi

  • Acoustic Modeling of Speaking Styles and Emotional Expressions in HMM-Based Speech Synthesis

    Junichi Yamagishi;Koji Onishi;Takashi Masuko;Takao Kobayashi

  • An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features

    Keiichi Tokuda;Takashi Masuko;Tetsuya Yamada;Takao Kobayashi

  • A Style Control Technique for HMM-Based Expressive Speech Synthesis

    Takashi Nose;Junichi Yamagishi;Takashi Masuko;Takashi Masuko;Takao Kobayashi

  • Speaker adaptation for HMM-based speech synthesis system using MLLR.

    Masatsune Tamura;Takashi Masuko;Keiichi Tokuda;Takao Kobayashi

  • Speech Synthesis with Various Emotional Expressions and Speaking Styles by Style Interpolation and Morphing

    Makoto Tachibana;Junichi Yamagishi;Takashi Masuko;Takao Kobayashi

  • Eigenvoices for HMM-based speech synthesis

    Kengo Shichiri;Atsushi Sawabe;Takayoshi Yoshimura;Keiichi Tokuda

  • Voice characteristics conversion for HMM-based speech synthesis system

    T. Masuko;K. Tokuda;T. Kobayashi;S. Imai

Frequent Co-Authors

Takao Kobayashi
Takao Kobayashi Tokyo Institute of Technology
Keiichi Tokuda
Keiichi Tokuda Nagoya Institute of Technology
Junichi Yamagishi
Junichi Yamagishi National Institute of Informatics
Heiga Zen
Heiga Zen Google (United States)
Alan W. Black
Alan W. Black Carnegie Mellon University
Tomoki Toda
Tomoki Toda Nagoya University

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