Kazuhiro Takemoto - Publications

Journal Articles | Conference Papers | Technical Reports | Book Chapters & Reviews | Books | Others

Journal Articles

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  1. Takemoto, K. Steering cooperation: Adversarial attacks on prisoner's dilemma in complex networks. Physica A, vol. 655, article no. 130214, 11 pp. (2024).
  2. Dzekashu, F. F., Yusuf, A. A., Takemoto, K., Peters, M. K., Lattorff, H. M. G., Steffan-Dewenter, I. and Pirk, C. W. W. Network resilience of plant-bee interactions in the Eastern Afromontane Biodiversity Hotspot. Ecological Indicators, vol. 166, article no. 112415, 14 pp. (2024).
  3. Takemoto, K. All in how you ask for it: Simple black-box method for jailbreak attacks. Applied Sciences, vol. 14, issue 9, article no. 3558, 14 pp. (2024).
  4. Takemoto, K. The moral machine experiment on large language models. Royal Society Open Science, vol. 11, issue 2, article no. 231393, 8 pp. (2024). Paper introduction by Aska studio | Research summary at Montreal AI Ethics Institute
  5. Fujimoto, S. and Takemoto, K. Revisiting the political biases of ChatGPT. Frontiers in Artificial Intelligence, vol. 6, article no. 1232003, 6 pp. (2023). Paper introduction by Aska studio | Research summary at Montreal AI Ethics Institute
  6. Chiyomaru, K. and Takemoto, K. Mitigation of adversarial attacks on voter model dynamics by network heterogeneity. Journal of Physics: Complexity, vol. 4, no. 2, article no. 025009, 9 pp. (2023).
  7. Matsuo, Y. and Takemoto, K. Backdoor attacks on deep neural networks via transfer learning from natural images. Applied Sciences, vol. 12, issue 24, article no. 12564, 9 pp. (2022).
  8. Chiyomaru, K. and Takemoto, K. Adversarial attacks on voter model dynamics in complex networks. Physical Review E, vol. 106, no. 1, article no. 014301, 6 pp. (2022).
  9. Koga, K. and Takemoto, K. Simple black-box universal adversarial attacks on deep neural networks for medical image classification. Algorithms, vol. 15, issue 5, article no. 144, 12 pp. (2022).
  10. Minagi, A., Hirano, H. and Takemoto K. Natural images allow universal adversarial attacks on medical image classification using deep neural networks with transfer learning. Journal of Imaging, vol. 8, issue 2, article no. 38, 15 pp. (2022).
  11. Matsuo, Y. and Takemoto, K. Backdoor attacks to deep neural network-based system for COVID-19 detection from chest X-ray images. Applied Sciences, vol. 11, issue 20, article no. 9556, 10 pp. (2021).
  12. Kanzaki, Y. and Takemoto, K. Diversity of dominant soil bacteria increases with warming velocity at the global scale. Diversity, vol. 13, issue 3, article no. 120, 11 pp. (2021).
  13. Hirano, H., Minagi, A. and Takemoto, K. Universal adversarial attacks on deep neural networks for medical image classification. BMC Medical Imaging, vol. 21, article no. 9, 13 pp. (2021).
  14. Hirano, H., Koga, K. and Takemoto, K. Vulnerability of deep neural networks for detecting COVID-19 cases from chest X-ray images to universal adversarial attacks. PLoS ONE, vol. 15, no. 12, article no. e0243963, 15 pp. (2020).
  15. Hirano, H. and Takemoto, K. Simple iterative method for generating targeted universal adversarial perturbations. Algorithms, vol. 13, issue 11, article no. 268, 10 pp. (2020). Preliminary version has appeared in AROB 25th 2020.
  16. Ueda, I., Takemoto, K., Watanabe, K., Sugimoto, K., Ikenouchi, A., Kakeda, S., Katsuki, A., Yoshimura, R. and Korogi, Y. The brain-derived neurotrophic factor Val66Met polymorphism increases segregation of structural correlation networks in healthy adult brains. PeerJ, vol. 8, article no. e9632, 24 pp. (2020).
  17. Chiyomaru, K. and Takemoto, K. Revisiting the hypothesis of an energetic barrier to genome complexity between eukaryotes and prokaryotes. Royal Society Open Science, vol. 7, issue 2, article no. 191859, 9 pp. (2020).
  18. Hirano, H. and Takemoto, K. Difficulty in inferring microbial community structure based on co-occurrence network approaches. BMC Bioinformatics, vol. 20, article no. 329, 14 pp. (2019).
  19. Ueda, I., Kakeda, S., Watanabe, K., Sugimoto, K., Igata, N., Moriya, J., Takemoto, K., Katsuki, A., Yoshimura, R., Abe, O. and Korogi, Y. Brain structural connectivity and neuroticism in healthy adults. Scientific Reports, vol. 8, article no. 16491, pp. 8 (2018).
  20. Nagaishi, E. and Takemoto, K. Network resilience of mutualistic ecosystems and environmental changes: an empirical study. Royal Society Open Science, vol. 5, issue 9, article no. 180706, 12 pp. (2018).
  21. Dobashi, T., Iida, M. and Takemoto K. Decomposing the effects of ocean environments on predator--prey body-size relationships in food webs. Royal Society Open Science, vol. 5, issue 7, article no. 180707, 10 pp. (2018).
  22. Iida, M. and Takemoto, K. A network biology-based approach to evaluating the effect of environmental contaminants on human interactome and diseases. Ecotoxicology and Environmental Safety, vol. 160, pp. 316-327 (2018).
  23. Arai, W., Taniguchi, T., Goto, S., Moriya, Y., Uehara, H., Takemoto, K., Ogata, H. and Takami, H. MAPLE 2.3.0: An improved system for evaluating the functionomes of genomes and metagenomes. Bioscience, Biotechnology, and Biochemistry, vol. 82, no. 9, pp. 1515-1517 (2018).
  24. Song, J., Li, F., Takemoto, K., Haffari, G., Akutsu, T., Chou, K.-C. and Webb, G. I. PREvaIL, an integrative approach for inferring catalytic residues using sequence, structural and network features in a machine learning framework. Journal of Theoretical Biology, vol. 443, pp. 125-137 (2018).
  25. Uemura, E., Niwa, T., Minami, S., Takemoto, K., Fukuchi, S., Machida, K., Imataka, H., Ueda, T., Ota, M. and Taguchi, H. Large-scale aggregation analysis of eukaryotic cytosolic proteins reveals an involvement of intrinsically disordered regions in protein folding. Scientific Reports, vol. 8, article no. 678, 11pp. (2018).
  26. Takemoto, K. and Aie, K. Limitations of a metabolic network-based reverse ecology method for inferring host--pathogen interactions. BMC Bioinformatics, vol. 18, article no. 278, 9 pp. (2017).
  27. Takemoto, K. and Imoto, M. Exosomes in mammals with greater habitat variability contain more proteins and RNAs. Royal Society Open Science, vol. 4, issue 4, article no. 170162, pp. 7 (2017).
  28. Muto-Fujita, A.#, Takemoto, K.#, Kanaya, S., Nakazato, T., Tokimatsu, T., Matsumoto, N., Kono, M., Chubachi, Y., Ozaki, K. and Kotera, M. Data integration aids understanding of butterfly--host plant networks. Scientific Reports, vol. 7, article no. 43368, 14 pp. (2017).#Equal contribution
  29. Takami, H., Tanigichi, T., Arai, W., Takemoto, K., Moriya, Y. and Goto, S. An automated system for evaluation of the potential functionome: MAPLE version 2.1.0. DNA Research, vol. 23, no. 5, pp. 467-475 (2016).
  30. Takemoto, K., Ii, M. and Nishizuka, S. S. Importance of metabolic rate to the relationship between the number of genes in a functional category and body size in Peto's paradox for cancer. Royal Society Open Science, vol. 3, issue 9, article no. 160267, 11 pp. (2016).
  31. Takemoto, K.# and Akutsu, T.# Analysis of the effect of degree correlation on the size of minimum dominating sets in complex networks. PLoS ONE, vol. 11, no. 6, article no. e0157868, 11 pp. (2016). #Equal contribution
  32. Takemoto, K. and Kajihara, K. Human impacts and climate change influence nestedness and modularity in food-web and mutualistic networks. PLoS ONE, vol. 11, no. 6, article no. e0157929, 16 pp. (2016). This article is included in PLOS Ecological Impacts of Climate Change Collection.
  33. Kume, K., Ishida, K., Ikeda, M., Takemoto, K., Shimura, T., Young, L. and Nishizuka, S. S. Systematic protein level regulation via degradation machinery induced by genotoxic drugs. Journal of Proteome Research, vol. 15, no. 1, pp. 205-215 (2016).
  34. Takemoto, K. Habitat variability does not generally promote metabolic network modularity in flies and mammals. Biosystems, vol. 139, pp. 46-54 (2016).
  35. Takami, H., Arai, W., Takemoto, K., Uchiyama, I. and Taniguchi, T. Functional classification of uncultured "Candidatus Caldiarchaeum subterraneum" using the Maple system. PLoS ONE, vol. 10, no. 7, article no. e0132994, 18 pp. (2015).
  36. Takemoto, K. and Kawakami, Y. The proportion of genes in a functional category is linked to mass-specific metabolic rate and lifespan. Scientific Reports, vol. 5, article no. 10008, 10 pp. (2015).
  37. Takemoto, K. Heterogeneity of cells may explain allometric scaling of metabolic rate. Biosystems, vol. 130, pp. 11-16 (2015).
  38. Takemoto, K. Metabolic networks are almost nonfractal: A comprehensive evaluation. Physical Review E, vol. 90, issue 2, article no. 022802, 6 pp. (2014).
  39. Feng, W. and Takemoto, K. Heterogeneity in ecological mutualistic networks dominantly determines community stability. Scientific Reports, vol. 4, article no. 5912, 11 pp. (2014). Network data and source code | Erratum
  40. Takemoto, K., Kanamaru, S. and Feng, W. Climatic seasonality may affect ecological network structure: Food webs and mutualistic networks. Biosystems, vol. 121, pp. 29-37 (2014).
  41. Takemoto, K. and Yoshitake, I. Limited influence of oxygen on the evolution of chemical diversity in metabolic networks. Metabolites, vol. 3, issue 4, pp. 979-992 (2013).
  42. Takemoto, K., Tamura, T. and Akutsu, T. Theoretical estimation of metabolic network robustness against multiple reaction knockouts using branching process approximation. Physica A, vol. 392, issue 21, pp. 5525-5535 (2013).
  43. Takemoto, K. and Kihara, K. Modular organization of cancer signaling networks is associated with patient survivability. Biosystems, vol. 113, issue 3, pp. 149-154 (2013).
  44. Takemoto, K. Does habitat variability really promote metabolic network modularity? PLoS ONE, vol. 8, no. 4, article no. e61348, 9 pp. (2013).
  45. Zheng, C., Wang, M., Takemoto, K., Akutsu, T., Zhang, Z. and Song, J. An integrative computational framework based on a two-step random forest algorithm improves prediction of zinc-binding sites in proteins. PLoS ONE, vol. 7, no. 11, article no. e49716, 15 pp. (2012).
  46. Takemoto, K. Metabolic network modularity arising from simple growth processes. Physical Review E, vol. 86, issue 3, article no. 036107, 9 pp. (2012).
  47. Tabei, Y., Pauwels, E., Stoven, V., Takemoto, K. and Yamanishi, Y. Identification of chemogenomic features from drug-target interaction networks using interpretable classifiers. Bioinformatics, vol. 28, issue 18, pp. i487-i494 (2012). This article appears in ECCB2012 proceedings papers committee.
  48. Wang, M., Zhao, X.-M., Takemoto, K., Xu, H., Li, Y., Akutsu, T. and Song, J. FunSAV: predicting the functional effect of single amino acid variants using a two-stage random forest model. PLoS ONE, vol. 7, no. 8, article no. e43847, 14 pp. (2012).
  49. Takemoto, K., Tamura, T., Cong, Y., Ching, W.-K., Vert, J.-P. and Akutsu, T. Analysis of the impact degree distribution in metabolic networks using branching process approximation. Physica A, vol. 391, issue 1-2, pp. 379-387 (2012).
  50. Takemoto, K. and Borjigin, S. Metabolic network modularity in Archaea depends on growth conditions. PLoS ONE, vol. 6, no. 10, article no. e25874, 6 pp. (2011).
  51. Takemoto, K., Niwa, T. and Taguchi, H. Difference in the distribution pattern of substrate enzymes in the metabolic network of Escherichia coli, according to chaperonin requirement. BMC Systems Biology, vol. 5, article no. 98, 12 pp. (2011).
  52. Takemoto, K. and Arita, M. Nested structure acquired through simple evolutionary process. Journal of Theoretical Biology, vol. 264, issue 3, pp. 782-786 (2010).
  53. Song, J., Takemoto, K., Shen, H., Tan, H., Gromiha, M.M. and Akutsu, T. Prediction of protein folding rates from structural topology and complex network properties. IPSJ Transactions on Bioinformatics, vol. 3, pp. 40-53 (2010).
  54. Takemoto, K. Global architecture of metabolite distributions across species and its formation mechanisms. Biosystems, vol. 100, issue 1, pp. 8-13 (2010).
  55. Tamura, T., Takemoto, K. and Akutsu, T. Finding minimum reaction cuts of metabolic networks under a Boolean model using integer programming and feedback vertex sets. International Journal of Knowledge Discovery in Bioinformatics, vol. 1, issue 1, pp. 14-31 (2010).
  56. Takemoto, K. and Arita, M. Heterogeneous distribution of metabolites across plant species. Physica A, vol. 388, issue 13, pp. 2771-2780 (2009).
  57. Takemoto, K. and Akutsu, T. Origin of structural difference in metabolic networks with respect to temperature. BMC Systems Biology, vol. 2, article no. 82, 13 pp. (2008).
  58. Song, J., Tan, H., Takemoto, K. and Akutsu, T. HSEpred: predict Half-Sphere Exposure from protein sequences. Bioinformatics, vol. 24, issue 13, pp. 1489-1497 (2008).
  59. Takemoto, K., Nacher, J.C. and Akutsu, T. Correlation between structure and temperature in prokaryotic metabolic networks. BMC Bioinformatics, vol. 8, article no. 303, 11 pp. (2007).
  60. Takemoto, K. and Oosawa, C. Modeling for evolving biological networks with scale-free connectivity, hierarchical modularity, and disassortativity. Mathematical Biosciences, vol. 208, issue 2, pp. 454-468 (2007).
  61. Takemoto, K., Oosawa, C. and Akutsu, T. Structure of n-clique networks embedded in a complex network. Physica A, vol. 380, pp. 665-672 (2007).
  62. Takemoto, K. and Oosawa, C. Evolving networks by merging cliques. Physical Review E, vol. 72, issue 4, article no. 046116, 7 pp. (2005).

Conference Papers

  1. Hirano, H. and Takemoto, K. Simple iterative method for generating targeted universal adversarial perturbations. in Proceedings of 25th International Symposium on Artificial Life and Robotics (AROB 25th 2020), pp. 426-430 (2020). 22-24 January 2020, B-Con Plaza, Beppu, Japan.
  2. Tamura, T., Takemoto, K. and Akutsu, T. Measuring structural robustness of metabolic networks under a Boolean model using integer programming and feedback vertex sets. in Proceedings of the Second International Workshop on Intelligent Informatics in Biology and Medicine (IIBM2009), pp. 819-824 (2009). 16-19 March 2009, Fukuoka Institute of Technology, Fukuoka, Japan.
  3. Tamura, T., Chiristian, N., Takemoto, K., Ebenhöh, O. and Akutsu, T. Analysis and prediction of nutritional requirements using structural properties of metabolic networks and support vector machines. Genome Informatics, vol. 22, pp. 176-190 (2009). The Ninth Annual International Workshop on Bioinformatics and Systems Biology (IBSB2009), 27-29 July 2009, Boston, MA, USA.
  4. Oosawa, C., Takemoto, K. and Savageau, M.A. Feedback and feedforward loops have opposite effects on dynamics of transcriptional regulatory model networks. in Proceedings of the 13th International Symposium on Artificial Life and Robotics (AROB 13th '08), pp. 885-890 (2008). 31 January - 2 February 2008, B-Con Plaza, Beppu, Japan.
  5. Oosawa, C., Takemoto, K., Matsumoto, S. and Savageau, M.A. Local cause of coherence in Boolean networks. in Proceedings of the 12th International Symposium on Artificial Life and Robotics (AROB 12th '07), pp. 621-626 (2007). 25-27 January 2007, B-Con Plaza, Beppu, Japan.
  6. 竹本和広, ホセ・カルロス・ナチェル, 阿久津達也. 原核生物の代謝ネットワークにおける構造の乱雑さと生育温度の関係. 日本ソフトウェア科学会研究会資料, no. 44, pp. 31-37 (2006年). 日本ソフトウェア科学会 ネットワークが創発する知能研究会第2回国内ワークショップ (JWEIN2006), 2006年9月27～29日, ニュー阿寒ホテル.

Technical Reports

  1. Tamura, T., Takemoto, K. and Akutsu, T. Algorithm for finding minimum reaction cut of metabolic network. IEICE Technical Report, vol. 109, no. 235, pp. 27-34 (2009). 電子情報通信学会コンピュテーション研究会 (2009-10-COMP), 2009年10月16日, 東北大学青葉山キャンパス.
  2. Tamura, T., Takemoto, K. and Akutsu, T. Measuring structural robustness of metabolic networks using integer programming and feedback vertex sets. IPSJ SIG technical reports, vol. 2008, no. 86, pp. 13-16 (2008). 情報処理学会第14回バイオ情報学研究会, 2008年9月18～19日, 北海道大学.
  3. 大澤智興, 竹本和広. ブーリアンネットワークにおけるモチーフ構造の埋め込みの効果. 電子情報通信学会技術研究報告, vol. 107, no. 349, pp. 1-6 (2007). 電子通信学会非線形問題研究会 (2007-11-NPL), 2007年11月22日, 九州大学伊都キャンパス.
  4. 竹本和広, ホセ・C・ナチェル, 阿久津達也. 生育温度による代謝ネットワーク構造の差異. 情報処理学会研究報告, vol. 2007, no. 89, pp. 9-16 (2007). 情報処理学会第10回バイオ情報学研究会, 2007年9月13～14日, はこだて未来大学.
  5. 大澤智興, 竹本和広. 少数のノードからなるブーリアンサブネットワークの伝達特性. 電子情報通信学会技術研究報告, vol. 105, no. 417, pp. 13-18 (2005). 電子通信学会非線形問題研究会 (2005-11-NPL), 2005年11月18～19日, 九州工業大学大学院若松キャンパス.
  6. 大澤智興, 竹本和広. スケールフリーランダムブーリアンネットワークの力学的性質に対するブール関数の組み合わせ依存性. 電子情報通信学会技術研究報告, vol. 105, no. 417, pp. 19-24 (2005). 電子通信学会非線形問題研究会 (2005-11-NPL), 2005年11月18～19日, 九州工業大学大学院若松キャンパス.

Book Chapters & Reviews

  1. 竹本和広. ビッグデータとAIが拓く新時代のバイオインフォマティクスー医療と創薬のAI新時代. 化学と工業, vol. 77, no. 4, pp. 265-267 (2024). 特集 バイオインフォマティクス研究の今 収録
  2. 千代丸勝美, 竹本和広. ネットワーク伝播による生物ネットワーク解析. JSBi Bioinformatics Review, vol. 1, no. 2, pp. 26-36 (2021).
  3. Takemoto, K. and Iida, M. Ecological networks. In Encyclopedia of Bioinformatics and Computational Biology (eds. Ranganathan, S., Nakai, K., Schönbach C. and Gribskov, M.), Elsevier, pp. 1131-1141 (2019).
  4. 竹本和広. 代謝ネットワークを用いた微生物生態系の可視化. 生命科学で使える はじめての数理モデルとシミュレーション 収録 (実験医学増刊, vol. 35, no. 5), 羊土社, pp. 211-214 (2017).
  5. 竹本和広. 代謝ネットワークの数理モデルとその応用. 応用数理, vol. 24, no. 1, pp. 10-18 (2014).
  6. 竹本和広. 代謝ネットワークのロバストネス. 細胞工学, vol. 33, no. 1, pp. 31-36 (2014). 特集 生命システムのロバストネスとは何か？ 収録
  7. Takemoto, K. Current understanding of the formation and adaptation of metabolic systems based on network theory. Metabolites, vol. 2, issue 3, pp. 429-457 (2012). This article belongs to the special issue Metabolic Network Models.
  8. Takemoto, K. and Oosawa, C. Modeling for evolving biological networks. In Statistical and Machine Learning Approaches for Network Analysis (eds. Dehmer, M. and Basak, S.C.), John Wiley & Sons, pp. 77-108 (2012).
  9. Takemoto, K. and Oosawa, C. Introduction to complex networks: Measures, statistical properties, and models. In Statistical and Machine Learning Approaches for Network Analysis (eds. Dehmer, M. and Basak, S.C.), John Wiley & Sons, pp. 45-75 (2012).
  10. 竹本和広. 代謝ネットワーク形成の理解に向けて. 細胞を創る・生命システムを創る 収録 (実験医学増刊, vol. 29, no. 7), 羊土社, pp. 180-185 (2011).

Books

  1. 竹本和広 著. 生物ネットワーク解析（浜田道昭 監修 バイオインフォマティクスシリーズ2）. コロナ社, 2021.
  2. Antao, T. 著 / 阿久津達也, 竹本和広 訳. バイオインフォマティクス ―Pythonによる実践レシピ―. 朝倉出版, 2020.
  3. 日本バイオインフォマティクス学会 編. バイオインフォマティクス入門. 慶應義塾大学出版会, 2015 (竹本は6-6, 6-7, 6-8節を担当).

Others

  1. Chiyomaru, K., Kanzaki, Y. and Takemoto, K. Global COVID-19 transmission rate is influenced by precipitation seasonality and the speed of climate temperature warming. medRxiv 2020.04.10.20060459. doi:10.1101/2020.04.10.20060459.
  2. 竹本和広. 点と線. 生物工学会誌, vol. 90, no. 3, p. 148 (2012).
  3. 竹本和広. APBC2010参加レポート. 日本バイオインフォマティクス学会ニュースレター, no. 20, pp.15-16 (2010).