東北大学　志賀研究室 > 雑誌論文
  1. Visual explanations of machine learning model estimating charge states in quantum dots
    Yui Muto, Takumi Nakaso, Motoya Shinozaki, Takumi Aizawa, Takahito Kitada, Takashi Nakajima, Matthieu R. Delbecq, Jun Yoneda, Kenta Takeda, Akito Noiri, Arne Ludwig, Andreas D. Wieck, Seigo Tarucha, Atsunori Kanemura, Motoki Shiga, Tomohiro Otsuka,
    APL Machine Learning, 2 (2), 026110, 2024. [doi]
  2. Atomic and Electronic Structure in MgO–SiO2
    Yuta Shuseki, Shinji Kohara, Tomoaki Kaneko, Keitaro Sodeyama, Yohei Onodera, Chihiro Koyama, Atsunobu Masuno, Shunta Sasaki, Shohei Hatano, Motoki Shiga, Ippei Obayashi, Yasuaki Hiraoka, Junpei T. Okada, Akitoshi Mizuno, Yuki Watanabe, Yui Nakata, Koji Ohara, Motohiko Murakami, Matthew G. Tucker, Marshall T. McDonnell, Hirohisa Oda, and Takehiko Ishikawa,
    The Journal of Physical Chemistry A, 128, 4, 716–726 2024. [doi]
  3. Ring-originated anisotropy of local structural ordering in amorphous and crystalline silicon dioxide
    Motoki Shiga, Akihiko Hirata, Yohei Onodera, Hirokazu Masai,
    Communications Materials, 4, 91, 2023. [doi]
    (arXiv:2209.12116, 2022. [preprint])
  4. Ring compaction as a mechanism of densification in amorphous silica
    Philip S. Salmon, Anita Zeidler, Motoki Shiga, Yohei Onodera, Shinji Kohara,
    Physical Review B, 107, 144203, 2023. [doi]
  5. Local structure analysis of disordered materials via contrast variation in scanning transmission electron microscopy
    Koji Kimoto, Motoki Shiga, Shinji Kohara, Jun Kikkawa, Ovidiu Cretu, Yohei Onodera, Kazuo Ishizuka,
    AIP Advances, 12, 095219, 2022. [doi]
  6. A Generalized framework of multi-fidelity max-value entropy search through joint entropy
    Shion Takeno, Hitoshi Fukuoka, Yuki Tsukada, Toshiyuki Koyama, Motoki Shiga, Ichiro Takeuchi, and Masayuki Karasuyama
    Neural Computation, 34 (10), 2145–2203, 2022.
  7. Relationship between diffraction peak, network topology, and amorphous-forming ability in silicon and silica
    Shinji Kohara, Motoki Shiga, Yohei Onodera, Hirokazu Masai, Akihiko Hirata, Motohiko Murakami, Tetsuya Morishita, Koji Kimura, Kouichi Hayashi
    Scientific Reports, 11, 22180, 2021.
  8. Accelerated discovery of proton-conducting perovskite oxide by capturing physicochemical fundamentals of hydration
    Jyunji Hyodo, Kota Tsujikawa, Motoki Shiga, Yuji Okuyama, Yoshihiro Yamazaki
    ACS Energy Letters, 6, 2985–2992, 2021.
  9. 機械学習を用いたペロブスカイト酸化物におけるプロトン濃度の予測精度の評価
    辻川皓太, 兵頭潤次, 志賀元紀, 奥山勇治, 山崎仁丈
    燃料電池, 20, 75–86, 2021.
  10. Structure and properties of densified silica glass: characterizing the order within disorder
    Yohei Onodera, Shinji Kohara, Philip S. Salmon, Akihiko Hirata, Norimasa Nishiyama, Suguru Kitani, Anita Zeidler, Motoki Shiga, Atsunobu Masuno, Hiroyuki Inoue, Shuta Tahara, Annalisa Polidori, Henry E. Fischer, Tatsuya Mori, Seiji Kojima, Hitoshi Kawaji, Alexander I. Kolesnikov, Matthew B. Stone, Matthew G. Tucker, Marshall T. McDonnell, Alex C. Hannon, Yasuaki Hiraoka, Ippei Obayashi, Takenobu Nakamura, Jaakko Akola, Yasuhiro Fujii, Koji Ohara, Takashi Taniguchi, Osami Sakata
    NPG Asia Materials , 12, 85, 2020.
  11. Cost-effective search for lower-error region in material parameter space using multifidelity Gaussian process modeling
    Shion Takeno, Yuhki Tsukada, Hitoshi Fukuoka, Toshiyuki Koyama, Motoki Shiga, Masayuki Karasuyama
    Physical Review Materials, 4, 083802, 2020.
  12. Application of machine learning techniques to electron microscopic/spectroscopic image data analysis
    Shunsuke Muto, Motoki Shiga
    Microscopy, 69 (2), 110–122, 2020.
  13. Understanding diffraction patterns of glassy, liquid and amorphous materials via persistent homology analyses
    Yohei Onodera, Shinji Kohara, Shuta Tahara, Atsunobu Masuno, Hiroyuki Inoue, Motoki Shiga, Akihiko Hirata, Koichi Tsuchiya, Yasuaki Hiraoka, Ippei Obayashi, Koji Ohara, Akitoshi Mizuno, Osami Sakata
    Journal of the Ceramic Society of Japan, Vol. 127, No.12, 853–863, 2019. (Cover)
  14. Estimation of material parameters based on precipitate shape: efficient identification of low-error region with Gaussian process modeling
    Yuhki Tsukada, Shion Takeno, Masayuki Karasuyama, Hitoshi Fukuoka, Motoki Shiga, Toshiyuki Koyama
    Scientific Reports, 9, 15794, 2019.
  15. Non-negative matrix factorization and its extensions for spectral image data analysis
    Motoki Shiga, Shunsuke Muto
    e-Journal of Surface Science and Nanotechnology, 17, 148–154, 2019.
  16. Community assessment to advance computational prediction of cancer drug combinations in a pharmacogenomic screen
    Michael Patrick Menden, Dennis Wang, Yuanfang Guan, Michael Mason, Bence Szalai, Krishna C Bulusu, Thomas Yu, Jaewoo Kang, Minji Jeon, Russ Wolfinger, Tin Nguyen, Mikhail Zaslavskiy, AstraZeneca-Sanger Drug Combination DREAM Consortium, In Sock Jang, Zara Ghazoui, Mehmet Eren Ahsen, Robert Vogel, Elias Chaibub Neto, Thea Norman, Eric KY Tang, Mathew J Garnett, Giovanni Di Veroli, Stephen Fawell, Gustavo Stolovitzky, Justin Guinney, Jonathan R Dry, Julio Saez-Rodriguez
    Nature Communications, Vol.10, 2674, 2019.
  17. 多変量解析を利用したTOF-SIMSイメージデータフュージョンとスパースモデリングおよび機械学習によるTOF-SIMSスペクトル解析
    石倉航, 高橋一真, 山㟁崇之, 青木弾, 福島和彦, 志賀元紀, 青柳里果
    Journal of Surface Analysis, Vol. 25, No.2, 103–114, 2018.
  18. A Crowdsourced Analysis to Identify ab Initio Molecular Signatures Predictive of Susceptibility to Viral Infection
    Slim Fourati, Aarthi Talla, Mehrad Mahmoudian, Joshua G Burkhart, Riku Klen, Ricardo Henao, Zafer Aydin, Ka Yee Yeung, Mehmet Eren Ahsen, Reem Almugbel, Samad Jahandideh, Xiao Liang, Torbjorn E.M. Nordling, Motoki Shiga, Ana Stanescu, Robert Vogel, The Respiratory Viral DREAM Challenge Consortium, Gaurav Pandey, Christopher Chiu, Micah T McClain, Chris W Woods, Geoffrey S Ginsburg, Laura L Elo, Ephraim L Tsalik, Lara M Mangravite, Solveig K Sieberts
    Nature Communications, 9, 4418, 2018.
  19. Informatics-Aided Raman Microscopy for Nanometric 3D Stress Characterization
    Hongxin Wang, Han Zhang , Bo Da, Motoki Shiga, Hideaki Kitazawa, Daisuke Fujita
    Journal of Physical Chemistry C, 122 (13), 7187–7193, 2018.
  20. Exploring a Potential Energy Surface by Machine Learning for Characterizing Atomic Transport
    Kenta Kanamori, Kazuaki Toyoura, Junya Honda, Atsuto Seko, Masayuki Karasuyama, Kazuki Shitara, Motoki Shiga, Akihide Kuwabara, Ichiro Takeuchi
    Physical Review B, 97, 125124, 2018.
  21. Time Variations of the Radial Velocity of H2O Masers in the Semi-Regular Variable R CRT
    Hiroshi Sudou*, Motoki Shiga*, Toshihiro Omodaka, Chihiro Nakai, Kazuki Ueda and Hiroshi Takaba (*第1著者: SudouとShiga)
    Journal of the Korean Astronomical Society, 50 (6), 157–165, 2017.
  22. Prediction of Overall Survival for Patients with Metastatic Castration-Resistant Prostate Cancer: Development of a Prognostic Model through a Crowdsourced Challenge with Open Clinical Trial Data
    Justin Guinney, Tao Wang, Teemu D Laajala, Kimberly Kanigel Winner, J Christopher Bare, Elias Chaibub Neto, Suleiman A Khan, Gopal Peddinti, Antti Airola, Tapio Pahikkala, Tuomas Mirtti, Thomas Yu, Brian M Bot, Liji Shen, Kald Abdallah, Thea Norman, Stephen Friend, Gustavo Stolovitzky, Howard Soule, Christopher J Sweeney, Charles J Ryan, Howard I Scher, Oliver Sartor, Yang Xie, Tero Aittokallio, Fang Liz Zhou, James C Costello, the Prostate Cancer Challenge DREAM Community
    The Lancet Oncology, 18 (1), 132–142, 2017.
  23. Two-step Feature Selection for Predicting Survival Time of Patients with Metastatic Castrate Resistant Prostate Cancer
    Motoki Shiga
    F1000Research, 5, 2678, 2016.
  24. Matrix Factorization for Automatic Chemical Mapping from Electron Microscopic Spectral Imaging Datasets
    Motoki Shiga, Shunsuke Muto, Kazuyoshi Tatsumi, Koji Tsuda
    Transactions of the Materials Research Society of Japan, 41 (4), 333–336, 2016.
  25. Sparse Modeling of EELS and EDX Spectral Imaging Data by Nonnegative Matrix Factorization
    Motoki Shiga, Kazuyoshi Tatsumi, Shunsuke Muto, Koji Tsuda, Yuta Yamamoto, Toshiyuki Mori, Takayoshi Tanji
    Ultramicroscopy, 170, 43–59, 2016.
  26. A machine learning-based selective sampling procedure for identifying the low energy region in a potential energy surface: a case study on proton conduction in oxides
    Kazuaki Toyoura, Daisuke Hirano, Atsuto Seko, Motoki Shiga, Akihide Kuwabara, Masayuki Karasuyama, Kazuki Shitara, Ichiro Takeuchi
    Physical Review B, 93 (5), 054112, 2016.
  27. Direct Conditional Probability Density Estimation with Sparse Feature Selection
    Motoki Shiga, Voot Tangkaratt, Masashi Sugiyama
    Machine Learning, 100 (2), 161–182, 2015. [コード]
  28. Non-negative Matrix Factorization with Auxiliary Information on Overlapping Groups
    Motoki Shiga, Hiroshi Mamitsuka
    IEEE Transactions on Knowledge and Data Engineering, 27 (6), 1615–1628, 2015.
  29. Detecting Differentially Coexpressed Genes from Labeled Expression Data: A Brief Review
    Mitsunori Kayano, Motoki Shiga, Hiroshi Mamitsuka
    IEEE/ACM Transactions on Computational Biology and Bioinformatics, 11 (1), 154–167, 2014.
  30. 化学構造の高速データマイニングのための特徴ベクトル TFS の圧縮法
    志賀元紀, 高橋由雅
    Journal of Computer Chemistry, Japan, 11 (2), 104–111, 2012.
  31. A Variational Bayesian Framework for Clustering with Multiple Graphs
    Motoki Shiga, Hiroshi Mamitsuka
    IEEE Transactions on Knowledge and Data Engineering, 24 (4), 577–590, 2012.
  32. Efficient Semi-Supervised Learning on Locally Informative Multiple Graphs
    Motoki Shiga, Hiroshi Mamitsuka
    Pattern Recognition, 45 (3), 1035–1049, 2012.
  33. Genome-wide Integration on Transcription Factors, Histone Acetylation and Gene Expression Reveals Genes Co-regulated by Histone Modification Patterns
    Yayoi Natsume-Kitatani, Motoki Shiga, Hiroshi Mamitsuka
    PLoS One, 6 (7), e22281, 2011.
  34. ROS-DET: Robust Detector of Switching Mechanisms in Gene Expression
    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
    Nucleic Acids Research, 39(11), e74, 2011.
  35. Clustering Genes with Expression and Beyond
    Motoki Shiga, Hiroshi Mamitsuka
    WIREs Data Mining and Knowledge Discovery, Invited paper, 1 (6), 496–511, 2011.
  36. A Spectral Approach to Clustering Numerical Vectors as Nodes in a Network
    Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
    Pattern Recognition, 44 (2), 236–251, 2011.
  37. On the Performance of Methods for Finding a Switching Mechanism in Gene Expression
    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
    Genome Informatics, 24, 69–83, 2010.
    (Proceedings of the 10th Annual International Workshop on Bioinformatics and Systems Biology IBSB2010)) 
  38. Efficiently Finding Genome-wide Three-way Gene Interactions from Transcript- and Genotype-Data
    Mitsunori Kayano, Ichigaku Takigawa, Motoki Shiga, Koji Tsuda, Hiroshi Mamitsuka
    Bioinformatics, 25, 2735–2743, 2009.
  39. Annotating Gene Functions with Integrative Spectral Clustering on Microarray Expressions and Sequences
    Limin Li, Motoki Shiga, Wai-ki Ching, Hiroshi Mamitsuka
    Genome Informatics, 22, 95–120, 2009.
    (Proceedings of the 9th Annual International Workshop on Bioinformatics and Systems Biology (IBSB2009))
  40. Upper Bound for Variational Free Energy of Bayesian Networks
    Kazuho Watanabe, Motoki Shiga, Sumio Watanabe
    Machine Learning, 75 (2), 199–215, 2009.
  41. 多様なゲノムデータの統合的クラスタリング解析
    志賀元紀, 瀧川一学, 馬見塚拓,
    生物物理, 理論/実験 技術, 48, 190–194, 2008.
  42. Mining Significant Tree Patterns in Carbohydrate Sugar Chains
    Kosuke Hashimoto, Ichigaku Takigawa, Motoki Shiga, Minoru Kanehisa, Hiroshi Mamitsuka
    Bioinformatics, 24, i167–i173, 2008.
    (Proceedings of the 7th European Conference on Computational Biology (ECCB2008))
  43. Annotating Gene Function by Combining Expression Data with a Modular Gene Network
    Motoki Shiga, Ichigaku Takigawa, Hiroshi Mamitsuka
    Bioinformatics, 23, i468–i478, 2007.
    (Proceedings of the 15th Annual International Conference on Intelligent Systems for Molecular Biology & 6th European Conference on Computational Biology
    (ISMB/ECCB2007)    )
  44. 推定を独立な標本から繰り返す場合に最適なエントロピー推定量
    志賀元紀, 横田康成,
    電気学会論文誌部門誌C, 研究開発レター, 125-C (12), 1912–1913, 2005.
  45. バイアス誤差の2乗平均を任意の値に制約する条件下で平均2乗誤差を最小化するエントロピー推定量
    志賀元紀, 横田康成,
    電子情報通信学会論文誌A, J88-A (4), 519–527, 2005.
  46. Effect of Time Division on Estimation Accuracy in Frequency Domain ICA
    Yasunari Yokota, Hideaki Iwata, Motoki Shiga
    IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, E87-A (12), 3424–3428, 2004.
  47. 平均2乗誤差を改善するエントロピー推定量
    横田康成, 志賀元紀,
    電子情報通信学会論文誌A, J86-A (9), 936–944, 2003.