Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data

Hirotomo Moriwaki, Shin Saito, Tomoya Matsumoto, Takayuki Serizawa, Ryo Kunimoto

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DOI: 10.1021/acsomega.2c00664
Published: 2022-05-23
Container: ACS Omega
Publisher: American Chemical Society (ACS)
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1021/acsomega.2c00664,
  title = {Global Analysis of Deep Learning Prediction Using
Large-Scale In-House Kinome-Wide Profiling Data},
  author = {Hirotomo Moriwaki and Shin Saito and Tomoya Matsumoto and Takayuki Serizawa and Ryo Kunimoto},
  year = {2022},
  journal = {ACS Omega},
  doi = {10.1021/acsomega.2c00664},
  url = {https://doi.org/10.1021/acsomega.2c00664}
}

RIS

TY  - JOUR
TI  - Global Analysis of Deep Learning Prediction Using
Large-Scale In-House Kinome-Wide Profiling Data
AU  - Hirotomo Moriwaki
AU  - Shin Saito
AU  - Tomoya Matsumoto
AU  - Takayuki Serizawa
AU  - Ryo Kunimoto
PY  - 2022
JO  - ACS Omega
DO  - 10.1021/acsomega.2c00664
UR  - https://doi.org/10.1021/acsomega.2c00664
ER  -

APA

Moriwaki, H., Saito, S., Matsumoto, T., Serizawa, T., & Kunimoto, R. (2022). Global Analysis of Deep Learning Prediction Using Large-Scale In-House Kinome-Wide Profiling Data. ACS Omega. https://doi.org/10.1021/acsomega.2c00664

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crossref · retrieved 2026-09-26T05:43:32.690Z