Masashi Tsubaki
Title
Cited by
Cited by
Year
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
1002019
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
282013
Mean-field theory of graph neural networks in graph partitioning
T Kawamoto, M Tsubaki, T Obuchi
Journal of Statistical Mechanics: Theory and Experiment 2019 (12), 124007, 2019
232019
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
162018
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
142019
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
122016
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
72020
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
62017
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
42020
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
32020
Learning excited states from ground states by using an artificial neural network
S Kiyohara, M Tsubaki, T Mizoguchi
npj Computational Materials 6 (1), 1-6, 2020
22020
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
2020
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
2020
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, 2020
2020
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
2019
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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Articles 1–16