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 | 100 | 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 | 28 | 2013 |

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 | 23 | 2019 |

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 | 16 | 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 | 14 | 2019 |

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 | 12 | 2016 |

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 | 7 | 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 | 6 | 2017 |

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 | 4 | 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 | 3 | 2020 |

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 | 2 | 2020 |

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 | | |