Product
Help
Pricing
About
Jobs
Log in
Get started
Author
Add to collection
Shuichiro Makigaki
Observed name:
Makigaki, Shuichiro
Observed institution:
Tokyo Institute of Technology
ORCID:
0000-0002-1988-2088
Observed ORCID:
0000-0002-1988-2088
OpenAlex ID:
A5027016084
Sequence alignment using machine learning for accurate template-based protein structure prediction
2019
·
Shuichiro Makigaki
, Takashi Ishida
·
Bioinformatics
·
26
PDF
Sequence alignment generation using intermediate sequence search for homology modeling
2020
·
Shuichiro Makigaki
, Takashi Ishida
·
Computational and Structural Biotechnology Journal
·
6
PDF
Sequence Alignment Using Machine Learning for Accurate Template-based Protein Structure Prediction
2020
·
Shuichiro Makigaki
, Takashi Ishida
·
BIO-PROTOCOL
·
4
Improvement of template-based protein structure prediction by using chimera alignment
2018
·
Shuichiro Makigaki
, Takashi Ishida
·
International Conference on Bioscience, Biochemistry and Bioinformatics (ICBBB)
·
2
Remote protein homolog detetction based on sequence similarity networks
2019
·
牧垣, 秀一朗
, Makigaki, Shuichiro
, et al.
·
Institutional Repositories DataBase (IRDB)
機械学習を用いたホモロジーモデリングのための配列アラインメント生成手法の高速化
2019
·
成井, 政人
, Narui, Masato
, et al.
·
Institutional Repositories DataBase (IRDB)
Acceleration of Machine Learning-based Sequence Alignment Generation
2019
·
成井, 政人
, Narui, Masato
, et al.
·
Institutional Repositories DataBase (IRDB)
配列類似性ネットワークに基づく高感度な遠縁タンパク質検索
2019
·
牧垣, 秀一朗
, Makigaki, Shuichiro
, et al.
·
Institutional Repositories DataBase (IRDB)
k最近傍法を用いた構造予測向け配列アラインメント生成手法の高速化
2019
·
成井, 政人
, 石田, 貴士
, et al.
·
Institutional Repositories DataBase (IRDB)
機械学習を用いたホモロジーモデリングのための配列アライメント手法
2019
·
牧垣, 秀一朗
, Makigaki, Shuichiro
, et al.
·
Institutional Repositories DataBase (IRDB)
Development of a protein sequence alignment method for accurate homology modeling
·
牧垣, 秀一朗
, Makigaki, Shuichiro
·
Institutional Repositories DataBase (IRDB)
PDF
Show more works
View as search
Works count:
18
Citation count:
38
H-index:
3
I10-index:
1
Year
Topic
Type