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Masashi Tsubaki
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Compound–protein interaction prediction with end-to-end learning of neural networks for graphs and sequences
M Tsubaki, K Tomii, J Sese
Bioinformatics 35 (2), 309-318, 2019
4932019
Quantum deep field: data-driven wave function, electron density generation, and atomization energy prediction and extrapolation with machine learning
M Tsubaki, T Mizoguchi
Physical Review Letters 125 (20), 206401, 2020
662020
Mean-field theory of graph neural networks in graph partitioning
T Kawamoto, M Tsubaki, T Obuchi
Advances in Neural Information Processing Systems 31, 2018
612018
Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer
K Asada, K Kobayashi, S Joutard, M Tubaki, S Takahashi, K Takasawa, ...
Biomolecules 10 (4), 524, 2020
432020
Dual graph convolutional neural network for predicting chemical networks
S Harada, H Akita, M Tsubaki, Y Baba, I Takigawa, Y Yamanishi, ...
BMC bioinformatics 21, 1-13, 2020
402020
Fast and accurate molecular property prediction: learning atomic interactions and potentials with neural networks
M Tsubaki, T Mizoguchi
The journal of physical chemistry letters 9 (19), 5733-5741, 2018
322018
Quantitative estimation of properties from core-loss spectrum via neural network
S Kiyohara, M Tsubaki, K Liao, T Mizoguchi
Journal of Physics: Materials 2 (2), 024003, 2019
312019
Modeling and learning semantic co-compositionality through prototype projections and neural networks
M Tsubaki, K Duh, M Shimbo, Y Matsumoto
Proceedings of the 2013 Conference on Empirical Methods in Natural Language …, 2013
312013
Non-linear similarity learning for compositionality
M Tsubaki, K Duh, M Shimbo, Y Matsumoto
Proceedings of the AAAI Conference on Artificial Intelligence 30 (1), 2016
202016
Learning excited states from ground states by using an artificial neural network
S Kiyohara, M Tsubaki, T Mizoguchi
Npj Computational Materials 6 (1), 68, 2020
182020
Comparing subject-to-subject transfer learning methods in surface electromyogram-based motion recognition with shallow and deep classifiers
T Hoshino, S Kanoga, M Tsubaki, A Aoyama
Neurocomputing 489, 599-612, 2022
172022
Quantum deep descriptor: Physically informed transfer learning from small molecules to polymers
M Tsubaki, T Mizoguchi
Journal of Chemical Theory and Computation 17 (12), 7814-7821, 2021
122021
On the equivalence of molecular graph convolution and molecular wave function with poor basis set
M Tsubaki, T Mizoguchi
Advances in Neural Information Processing Systems 33, 1982-1993, 2020
122020
Protein fold recognition with representation learning and long short-term memory
M Tsubaki, M Shimbo, Y Matsumoto
IPSJ Transactions on Bioinformatics 10, 2-8, 2017
82017
Analysis and usage: Subject-to-subject linear domain adaptation in sEMG classification
T Hoshino, S Kanoga, M Tsubaki, A Aoyama
2020 42nd Annual International Conference of the IEEE Engineering in …, 2020
22020
Correction to “Fast and Accurate Molecular Property Prediction: Learning Atomic Interactions and Potentials with Neural Networks”
M Tsubaki, T Mizoguchi
The Journal of Physical Chemistry Letters 10 (9), 2066-2067, 2019
22019
Prediction of ELNES and Quantification of Structural Properties Using Artificial Neural Network
S Kiyohara, M Tsubaki, T Mizoguchi
Microscopy and Microanalysis 26 (S2), 2100-2101, 2020
12020
Supplementary Material: On the equivalence of molecular graph convolution and molecular wave function with poor basis set
M Tsubaki, T Mizoguchi
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