Ohcanbohtosat - Ishida, Takashi

Aiddostahte ozu
  1. 1
    PrDOS: prediction of disordered protein regions from amino acid sequence

    PrDOS: prediction of disordered protein regions from amino acid sequence Dahkki Ishida, Takashi, Kinoshita, Kengo

    Almmustuhtton 2007
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  2. 2
    Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network

    Protein model accuracy estimation based on local structure quality assessment using 3D convolutional neural network Dahkki Sato, Rin, Ishida, Takashi

    Almmustuhtton 2019
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  3. 3
    Sequence alignment generation using intermediate sequence search for homology modeling

    Sequence alignment generation using intermediate sequence search for homology modeling Dahkki Makigaki, Shuichiro, Ishida, Takashi

    Almmustuhtton 2020
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  4. 4
    Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction

    Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction Dahkki Makigaki, Shuichiro, Ishida, Takashi

    Almmustuhtton 2020
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  5. 5
    P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features

    P3CMQA: Single-Model Quality Assessment Using 3DCNN with Profile-Based Features Dahkki Takei, Yuma, Ishida, Takashi

    Almmustuhtton 2021
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  6. 6
    End-to-end learning for compound activity prediction based on binding pocket information

    End-to-end learning for compound activity prediction based on binding pocket information Dahkki Tanebe, Toshitaka, Ishida, Takashi

    Almmustuhtton 2021
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  7. 7
    A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models

    A Benchmark Dataset for Evaluating Practical Performance of Model Quality Assessment of Homology Models Dahkki Takei, Yuma, Ishida, Takashi

    Almmustuhtton 2022
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  8. 8
    Protein Model Quality Estimation Using Molecular Dynamics Simulation

    Protein Model Quality Estimation Using Molecular Dynamics Simulation Dahkki Kurniawan, Jason, Ishida, Takashi

    Almmustuhtton 2022
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  9. 9
    Identification of transient hub proteins and the possible structural basis for their multiple interactions

    Identification of transient hub proteins and the possible structural basis for their multiple interactions Dahkki Higurashi, Miho, Ishida, Takashi, Kinoshita, Kengo

    Almmustuhtton 2008
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  10. 10
    Effects of surface-to-volume ratio of proteins on hydrophilic residues: Decrease in occurrence and increase in buried fraction

    Effects of surface-to-volume ratio of proteins on hydrophilic residues: Decrease in occurrence and increase in buried fraction Dahkki Shirota, Matsuyuki, Ishida, Takashi, Kinoshita, Kengo

    Almmustuhtton 2008
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  11. 11
    PiSite: a database of protein interaction sites using multiple binding states in the PDB

    PiSite: a database of protein interaction sites using multiple binding states in the PDB Dahkki Higurashi, Miho, Ishida, Takashi, Kinoshita, Kengo

    Almmustuhtton 2009
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  12. 12
    Analyses on hydrophobicity and attractiveness of all-atom distance-dependent potentials

    Analyses on hydrophobicity and attractiveness of all-atom distance-dependent potentials Dahkki Shirota, Matsuyuki, Ishida, Takashi, Kinoshita, Kengo

    Almmustuhtton 2009
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  13. 13
    Acceleration of sequence clustering using longest common subsequence filtering

    Acceleration of sequence clustering using longest common subsequence filtering Dahkki Namiki, Youhei, Ishida, Takashi, Akiyama, Yutaka

    Almmustuhtton 2013
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  14. 14
    Location, Location, Location: How Vascular Specialization Influences Hematopoietic Fates During Development

    Location, Location, Location: How Vascular Specialization Influences Hematopoietic Fates During Development Dahkki Heck, Adam M., Ishida, Takashi, Hadland, Brandon

    Almmustuhtton 2020
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  15. 15
    Helical microtubule arrays in a collection of twisting tubulin mutants of Arabidopsis thaliana

    Helical microtubule arrays in a collection of twisting tubulin mutants of Arabidopsis thaliana Dahkki Ishida, Takashi, Kaneko, Yayoi, Iwano, Megumi, Hashimoto, Takashi

    Almmustuhtton 2007
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  16. 16
    GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

    GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics Dahkki Suzuki, Shuji, Ishida, Takashi, Kurokawa, Ken, Akiyama, Yutaka

    Almmustuhtton 2012
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  17. 17
    MMS21/HPY2 and SIZ1, Two Arabidopsis SUMO E3 Ligases, Have Distinct Functions in Development

    MMS21/HPY2 and SIZ1, Two Arabidopsis SUMO E3 Ligases, Have Distinct Functions in Development Dahkki Ishida, Takashi, Yoshimura, Mika, Miura, Kenji, Sugimoto, Keiko

    Almmustuhtton 2012
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  18. 18
    GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array

    GHOSTX: An Improved Sequence Homology Search Algorithm Using a Query Suffix Array and a Database Suffix Array Dahkki Suzuki, Shuji, Kakuta, Masanori, Ishida, Takashi, Akiyama, Yutaka

    Almmustuhtton 2014
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  19. 19
    Faster sequence homology searches by clustering subsequences

    Faster sequence homology searches by clustering subsequences Dahkki Suzuki, Shuji, Kakuta, Masanori, Ishida, Takashi, Akiyama, Yutaka

    Almmustuhtton 2015
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:
  20. 20
    GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering

    GPU-Acceleration of Sequence Homology Searches with Database Subsequence Clustering Dahkki Suzuki, Shuji, Kakuta, Masanori, Ishida, Takashi, Akiyama, Yutaka

    Almmustuhtton 2016
    Viečča ollesdeavstta Viečča ollesdeavstta Viečča ollesdeavstta
    Teaksta
    Lasit oiddohiidda
    Furkejuvvon:

Ohcanreaiddut: