Masashi Tsubaki, Ph.D.
I am a researcher with the Machine Learning Research Team of the Artificial Intelligence Research Center (AIRC) at National Institute of Advanced Industrial Science and Technology (AIST), Japan.
I am interested in data-driven science with machine/deep learning for biology, chemistry, and physics.
Email: firstname.lastname@example.org, where x = tsubaki.masashi, or email@example.com, where y = masashi.tsubaki.814
Publications (Google Scholar)
Quantum deep field: data-driven wave function, electron density generation, and energy prediction and extrapolation with machine learning [paper] [code], Masashi Tsubaki and Teruyasu Mizoguchi, Physical Review Letters, 2020.
On the equivalence of molecular graph convolution and molecular wave function with poor basis set [paper] [code], Masashi Tsubaki and Teruyasu Mizoguchi, Advances in Neural Information Processing Systems (NeurIPS 2020).
Analysis and usage: subject-to-subject linear domain adaptation in sEMG classification [paper], Takayuki Hoshino, Suguru Kanoga, Masashi Tsubaki, and Atsushi Aoyama, 42nd Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC 2020).
Learning excited states from ground states by using an artificial neural network [paper], Shin Kiyohara, Masashi Tsubaki, and Teruyasu Mizoguchi, npj Computational Materials, 2020.
Dual graph convolutional neural network for predicting chemical networks [paper], Shonosuke Harada, Hirotaka Akita, Masashi Tsubaki, Yukino Baba, Ichigaku Takigawa, Yoshihiro Yamanishi, and Hisashi Kashima, BMC Bioinformatics, 2020.
Uncovering prognosis-related genes and pathways by multi-omics analysis in lung cancer [paper], Ken Asada, Kazuma Kobayashi, Samuel Joutard, Masashi Tsubaki, Satoshi Takahashi, Ken Takasawa, Masaaki Komatsu, Syuzo Kaneko, Jun Sese, and Ryuji Hamamoto, Biomolecules, 2020.
Quantitative estimation of properties from core-loss spectrum via neural network [paper], Shin Kiyohara, Masashi Tsubaki, Kunyen Liao, and Teruyasu Mizoguchi, Journal of Physics: Materials, 2019.
Mean-field theory of graph neural networks in graph partitioning [paper], Tatsuro Kawamoto, Masashi Tsubaki, and Tomoyuki Obuchi, Advances in Neural Information Processing Systems (NIPS 2018).
Fast and accurate molecular property prediction: learning atomic interactions and potentials with neural networks [paper] [code] [errata], Masashi Tsubaki and Teruyasu Mizoguchi, The Journal of Physical Chemistry Letters, 2018.
Compound-protein interaction prediction with end-to-end learning of neural networks for graphs and sequences [paper] [code1] [code2], Masashi Tsubaki, Kentaro Tomii, and Jun Sese, Bioinformatics, 2018.
Protein fold recognition with representation learning and long short-term memory [paper], Masashi Tsubaki, Masashi Shimbo, and Yuji Matsumoto, IPSJ Transactions on Bioinformatics, 2017.
Non-linear similarity learning for compositionality [paper], Masashi Tsubaki, Kevin Duh, Masashi Shimbo, and Yuji Matsumoto, The thirtieth AAAI Conference on Artificial Intelligence (AAAI 2016).
Modeling and learning semantic co-compositionality through prototype projections and neural networks [paper], Masashi Tsubaki, Kevin Duh, Masashi Shimbo, and Yuji Matsumoto, Conference on Empirical Methods in Natural Language Processing (EMNLP 2013).