K
Ken Asada
Researcher at Tokyo Medical and Dental University
Publications - 29
Citations - 845
Ken Asada is an academic researcher from Tokyo Medical and Dental University. The author has contributed to research in topics: Cancer & Computer science. The author has an hindex of 11, co-authored 29 publications receiving 500 citations. Previous affiliations of Ken Asada include Cornell University & University of Illinois at Chicago.
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Progesterone regulates cardiac repolarization through a nongenomic pathway: an in vitro patch-clamp and computational modeling study.
Hiroaki Nakamura,Junko Kurokawa,Chang Xi Bai,Ken Asada,Jun Xu,Ronit V. Oren,Zheng I. Zhu,Colleen E. Clancy,Mitsuaki Isobe,Tetsushi Furukawa +9 more
TL;DR: The data show that progesterone modulates cardiac repolarization by nitric oxide produced via a nongenomic pathway, which provides a framework to understand complex fluctuations of QT interval and torsade de pointes risks in various hormonal states in women.
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Application of Artificial Intelligence Technology in Oncology: Towards the Establishment of Precision Medicine
Ryuji Hamamoto,Kruthi Suvarna,Masayoshi Yamada,Kazuma Kobayashi,Norio Shinkai,Mototaka Miyake,Masamichi Takahashi,Shunichi Jinnai,Ryo Shimoyama,Akira Sakai,Ken Takasawa,Amina Bolatkan,Kanto Shozu,Ai Dozen,Hidenori Machino,Satoshi Takahashi,Ken Asada,Masaaki Komatsu,Jun Sese,Syuzo Kaneko +19 more
TL;DR: The history of AI technology as well as the state of the art of medical AI are introduced, focusing on the field of oncology, where AI is expected to play an important role in realizing the current global trend of precision medicine.
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Redox- and Calmodulin-dependent S-Nitrosylation of the KCNQ1 Channel
TL;DR: The data provide a molecular basis of NO-mediated regulation of the IKs channel and may play a role in previously demonstrated NO- mediated phenomenon in cardiac electrophysiology, including shortening in action potential duration in response to intracellular Ca2+ or sex hormones.
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Epigenetics Analysis and Integrated Analysis of Multiomics Data, Including Epigenetic Data, Using Artificial Intelligence in the Era of Precision Medicine.
TL;DR: The importance of genome-wide epigenetic and multiomics analyses using AI in the era of precision medicine is discussed and the current progress of artificial intelligence technologies, such as machine learning and deep learning, is remarkable and enables multimodal analyses of big omics data.
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Detection of Cardiac Structural Abnormalities in Fetal Ultrasound Videos Using Deep Learning
Masaaki Komatsu,Akira Sakai,Reina Komatsu,Ryu Matsuoka,Suguru Yasutomi,Kanto Shozu,Ai Dozen,Hidenori Machino,Hirokazu Hidaka,Tatsuya Arakaki,Ken Asada,Syuzo Kaneko,Akihiko Sekizawa,Ryuji Hamamoto +13 more
TL;DR: An architecture of Supervised Object detection with Normal data Only (SONO), based on a convolutional neural network (CNN), to detect cardiac substructures and structural abnormalities in fetal ultrasound videos, and shows an applicability to detects cardiac structural abnormalities.