Zobrazeno 1 - 10
of 19
pro vyhledávání: '"Masashi Tsubaki"'
Autor:
Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, Hisashi Kashima
Publikováno v:
BMC Bioinformatics, Vol 21, Iss S3, Pp 1-13 (2020)
Abstract Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the am
Externí odkaz:
https://doaj.org/article/2a2393e02fe04f9fbd6987b1c20df681
Publikováno v:
Neurocomputing. 489:599-612
Autor:
Masashi Tsubaki, Teruyasu Mizoguchi
Publikováno v:
Journal of Chemical Theory and Computation. 17:7814-7821
In this study, we propose a physically informed transfer learning approach for materials informatics (MI) using a quantum deep descriptor (QDD) obtained from the quantum deep field (QDF). The QDF is a machine learning model based on density functiona
Autor:
Masashi Tsubaki
Publikováno v:
The Brain & Neural Networks. 28:28-55
Publikováno v:
npj Computational Materials, Vol 6, Iss 1, Pp 1-6 (2020)
Excited states are different quantum states from their ground states, and spectroscopy methods that can assess excited states are widely used in materials characterization. Understanding the spectra reflecting excited states is thus of great importan
Autor:
Yoshihiro Yamanishi, Ichigaku Takigawa, Yukino Baba, Hirotaka Akita, Masashi Tsubaki, Shonosuke Harada, Hisashi Kashima
Publikováno v:
BMC Bioinformatics, Vol 21, Iss S3, Pp 1-13 (2020)
BMC Bioinformatics
BMC Bioinformatics
Background Predicting of chemical compounds is one of the fundamental tasks in bioinformatics and chemoinformatics, because it contributes to various applications in metabolic engineering and drug discovery. The recent rapid growth of the amount of a
Publikováno v:
EMBC
Before the operation of a biosignal-based application, long-duration calibration is required to adjust the pre-trained classifier to a new user data (target data). For reducing such time-consuming step, linear domain adaptation (DA) transfer learning
Autor:
Masashi Tsubaki, Teruyasu Mizoguchi
Deep neural networks (DNNs) have been used to successfully predict molecular properties calculated based on the Kohn--Sham density functional theory (KS-DFT). Although this prediction is fast and accurate, we believe that a DNN model for KS-DFT must
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::0b0b3c909ba9df372689b72e9fc8e669
Autor:
Masashi Tsubaki, Teruyasu Mizoguchi
Publikováno v:
The Journal of Physical Chemistry Letters. 9:5733-5741
The discovery of molecules with specific properties is crucial to developing effective materials and useful drugs. Recently, to accelerate such discoveries with machine learning, deep neural networks (DNNs) have been applied to quantum chemistry calc
Publikováno v:
Bioinformatics. 35:309-318
Motivation In bioinformatics, machine learning-based methods that predict the compound–protein interactions (CPIs) play an important role in the virtual screening for drug discovery. Recently, end-to-end representation learning for discrete symboli