| 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 | 636 | 2019 |
| 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 | 84 | 2020 |
| Mean-field theory of graph neural networks in graph partitioning T Kawamoto, M Tsubaki, T Obuchi Advances in neural information processing systems 31, 2018 | 74 | 2018 |
| 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 | 61 | 2020 |
| 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 | 49 | 2020 |
| 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 | 38 | 2018 |
| 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 | 36 | 2019 |
| 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 | 33 | 2013 |
| 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 | 29 | 2022 |
| 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 | 24 | 2020 |
| 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 | 19 | 2016 |
| 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 | 18 | 2021 |
| 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 | 14 | 2020 |
| 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 | 8 | 2017 |
| 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 | 2 | 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 | 2 | 2019 |
| 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 | 1 | 2020 |
| Comparison of fine-tuned single-source and multi-source approaches to surface electromyogram pattern recognition T Hoshino, S Kanoga, M Tsubaki, A Aoyama Biomedical Signal Processing and Control 94, 106261, 2024 | | 2024 |
| Supplementary Material: On the equivalence of molecular graph convolution and molecular wave function with poor basis set M Tsubaki, T Mizoguchi | | |
Teruyasu MizoguchiInstitute of Industrial Science, The University of Tokyoכתובת אימייל מאומתת בדומיין iis.u-tokyo.ac.jp