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Chen LI (李晨)

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Associate Professor

D3 Center, Osaka University
Graduate School of Information Science and Technology, Osaka University
Division of Electronic and Information Engineering, Faculty of Engineering, Osaka University

Research Interests: Deep Learning, Big Data

E-mail: li.chen.d3c@osaka-u.ac.jp

Publications

    Peer-Reviewed Journals

  1. S. Namba, N. Otani, C. Li, Y. Yamanishi, et al., “SSL-VQ: Vector-quantized variational autoencoders for semi-supervised prediction of therapeutic targets across diverse diseases,” Bioinformatics, 2025. [pdf]

  2. C. Li*, Y. Yamanishi, “AI-driven transcriptome profile-guided hit molecule generation,” Artificial Intelligence, 2024. [pdf]

  3. K. Yasuda, F. Berenger, K. Amaike, A. Ueda, T. Nakagomi, G. Hamasaki, C. Li, N. Y. Otani, K. Kaitoh, K. Tsuda, K. Itami, Y. Yamanishi*, “De novo generation of dual-target compounds using artificial intelligence,” iScience, 2024. [pdf]

  4. C. Li, Y. Zhu*, Y. Cao, J. Zhang, A. Annisa, D. Cheng, Y. Morimoto, “Mining area skyline objects from map-based big data using Apache Spark framework,” Array, 2024. [pdf]

  5. W. Ye, C. Li, W. Zhang, J. Li, L. Liu, D. Cheng, Z. Feng*, “Predicting drug-target interactions by measuring confidence with consistent causal neighborhood interventions,” Methods, 2024. [pdf]

  6. Y. Matsukiyo, A. Tengeiji, C. Li, and Y. Yamanishi*, "Transcriptionally conditional recurrent neural network for de novo drug design," Journal of Chemical Information and Modeling, 2024. [pdf]

  7. J. Zhang, Z. Wang, Z. Jiang, M. Wu*, C. Li*, and Y. Yamanishi, "Quantitative evaluation of molecular generation performance of graph-based GANs," Software Quality Journal, 2024. [pdf]

  8. C. Li, Y. Cao*, Y. Zhu, D. Cheng, C. Li, and Y. Morimoto, "Ripple knowledge graph convolutional networks for recommendation systems," Machine Intelligence Research, pp.1-14, 2024. [pdf]

  9. H. Tang*, C. Li*, S. Jiang, H. Yu, S. Kamei, Y. Yamanishi, and Y. Morimoto, "EarlGAN: An enhanced actor-critic reinforcement learning agent-driven GAN for de novo drug design," Pattern Recognition Letters, Vol.175, pp.45-51, 2023. [pdf]

  10. Q. Yu, C. Li*, Y. Zhu, and T. Kurita, "Convolutional autoencoder based on latent subspace projection for anomaly detection," Methods, Vol.214, pp.48-59, 2023. [pdf]

  11. A. Annisa*, M. R. Adyatma, G. I. Sampurno, C. Li, "Two-way recommendation system for supervisor selection using historical data and skyband-view queries," Journal of Advanced Research in Applied Sciences and Engineering Technology, Vol.34(2), pp.305-314, 2023. [pdf]

  12. J. Zhang*, Z. An, Z. Jiang, J. Du, Z. Yin, and C. Li*, "FedMCC: Federated multi-center clustering algorithm to improve privacy healthcare," Methods, Vol.218, pp.94-100, 2023. [pdf]

  13. C. Li* and J. Zheng, "深層学習のソフトウェア信頼性とサイバーセキュリティへの応用 [in Japanese]," オペレーションズ・リサーチ（特集：モノづくりDXに貢献するマネジメント技術), Vol.68(5), May, 2023. [pdf]

  14. C. Li*, J. Zheng, H. Okamura, and T. Dohi, "Performance evaluation of a cloud datacenter using CPU utilization data," Mathematics, Vol.11(3):513, January, 2023. [pdf]

  15. C. Li*, J. Zheng, H. Okamura, and T. Dohi, "Hierarchical Bayesian parameter estimation of queueing systems using utilization data," International Journal of Performability engineering, Vol.18(5), April, 2022. [pdf]

  16. C. Li*, J. Zheng, H. Okamura and T. Dohi, "Software reliability prediction through encoder-decoder Recurrent neural networks," International Journal of Mathematical, Engineering and Management Sciences, Vol.170, pp.73-82, March, 2022. [pdf]

  17. C. Li*, J. Zheng, H. Okamura, and T. Dohi, "Parameter estimation of Markovian arrivals with utilization data," IEICE Transactions on Communications, Vol.105(1), pp.1-10, April, 2021. [pdf]

  18. C. Li* and J. Zheng, "API call-based malware classification using recurrent neural networks," Journal of Cyber Security and Mobility, Vol.10(3), pp.617-640, May, 2021. [pdf]

  19. M. Qaosar*, A. Zaman, Md. A. Siddique, C. Li, and Y. Morimoto, "Secure k-skyband computation framework in distributed multi-party databases," Information Sciences, Vol.515, pp.388-403, April, 2020. [pdf]

  20. M. Qaosar*, K. Md. R. Alam, A. Zaman, C. Li, S. Ahmed, Md. A. Siddique, and Y. Morimoto, "A framework for privacy-preserving multi-party skyline query based on homomorphic encryption," IEEE Access, Vol.7, pp.167481-167496, November, 2019. [pdf]

  21. X. Zhang*, C. Li, and Y. Morimoto, "A multi-factor approach for stock price prediction by using recurrent neural networks," Bulletin of Networking, Computing, Systems, and Software (BNCSS), Vol.8, issue 1, pp.9-13, 2019. [pdf]

  22. S. Ahmed*, M. Qaosar, A. Zaman, Md. A. Siddique, C. Li, and Y. Morimoto, "Privacy aware MapReduce based multi-party secure skyline computation," Information, Vol.10, pp.1-19, June, 2019. [pdf]

  23. C. Li*, H. Okamura, and T. Dohi, "Parameter estimation of Mt/M/1/k queueing systems with utilization data," IEEE Access, Vol.7, pp.42664-42671, March, 2019. [pdf]

  24. C. Li*, Annisa, A. Zaman, M. Qaosar, S. Ahmed and Y. Morimoto, "MapReduce algorithm for location recommendation by using area skyline query," Algorithms, vol.11, pp.1-15, November, 2018. [pdf]

    25. Peer-Reviewed International Conferences

  25. C. Li, H. Tang, J. Zhang, X. Guo, D. Cheng, and Y. Morimoto, "Advancing aspect-based sentiment analysis through deep learning models," ADMA 2024, 2024. [pdf]

  26. H. Tang, C. Li, H. Yu, S. Kamei, and Y. Morimoto, "Tailored federated learning: leveraging direction regulation & knowledge distillation," ADMA 2024, 2024. [pdf]

  27. H. Tang, C. Li, and Y. Morimoto, "When molecular GAN meets byte-pair encoding," ADMA 2024, 2024. [pdf]

  28. C. Li* and Y. Yamanishi, "TenGAN: Pure transformer encoders make an efficient discrete GAN for de novo molecular generation," AISTATS 2024, 2024. [pdf]

  29. C. Li* and Y. Yamanishi, "GxVAEs: Two joint VAEs generate hit molecules from gene expression profiles," AAAI 2024, 2024. (Oral) [pdf]

  30. Z. Jiang, Z. Wang, J. Zhang, M. Wu*, C. Li*, and Y. Yamanishi, "Mode collapse alleviation of reinforcement learning-based GANs in drug design," 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023), 2023. (Oral) [pdf]

  31. W. Ye, C. Li, Y. Xie, W. Zhang, H. Zhang, B. Wang, D. Cheng*, and Z. Feng*, "Causal intervention for measuring confidence in drug-target interaction prediction," 2023 IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM 2023), 2023. (Oral) [pdf]

  32. C. Li* and Y. Yamanishi, "SpotGAN: A reverse-transformer GAN generates scaffold-constrained molecules with property optimization," In proceedings of the European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases 2023 (ECML-PKDD 2023), September, 2023. (Oral) [pdf]

  33. H. Tang*, C. Li*, S. Jiang, H. Yu, S. Kamei, Y. Yamanishi, and Y. Morimoto, "MacGAN: A moment-actor-critic reinforcement learning-based generative adversarial network for molecular generation," In proceedings of the 7th APWeb-WAIM International Joint Conference on Web and Big Data (APweb-WAIM 2023), October, 2023. (Oral) [pdf]

  34. J. Zhang*, Z. Jiang, Z. Wang, and C. Li, "Semi-supervised classification based on graph convolution encoder representations from BERT," In proceedings of the 19th International Conference on Advanced Data Mining and Applications 2023 (ADMA 2023), August, 2023. (Oral) [pdf]

  35. S. Jiang*, S. Kamei, C. Li, S. Hou, and Y. Morimoto, "A visual interpretation-based self-improved classification system using virtual adversarial training," In proceedings of the 19th International Conference on Advanced Data Mining and Applications 2023 (ADMA 2023), August, 2023. (Oral) [pdf]

  36. C. Li, Y. Cao*, Y. Zhu, J. Zhang, Annisa, D. Cheng, H. Tang, S. Jiang, K. Maruyama, and Y. Morimoto, "An enhanced distributed algorithm for area skyline computation based on Apache Spark," In proceedings of the 16th International Conference on Knowledge Science, Engineering and Management (KSEM 2023), August, 2023. (Oral) [pdf]

  37. Z. An*, Y. Tan, J. Zhang, Z. Jiang, and C. Li, "A session recommendation model based on heterogeneous graph neural network," In proceedings of the 16th International Conference on Knowledge Science, Engineering and Management (KSEM 2023), August, 2023. (Oral) [pdf]

  38. C. Li*, J. Zheng*, P. Ju, and Y. Morimoto, "Senti-EGCN: An aspect-based sentiment analysis system using edge-enhanced graph convolutional networks," In proceedings of the 10th International Conference on Dependable Systems and Their Applications (DSA 2023), August, 2023. (Oral) [pdf]

  39. C. Li*, K. Kaitoh, C. Yamanaka, and Y. Yamanishi, "Transformer-based objective-reinforced generative adversarial network to generate desired molecules," In proceedings of the 31st International Joint Conference on Artificial Intelligence (IJCAI 2022), April, 2022. (Oral) [pdf]

  40. C. Li*, Z. Chen, and J. Zheng, "An efficient transformer encoder-based classification of malware using API calls," In proceedings of the 24th IEEE International Conference on High Performance Computing & Communications (HPCC), 2022. (Oral) [pdf]

  41. H. Zhao*, Y. Zhang, Z. Feng, D. Cheng, and C. Li, "Matching using sufficient dimension reduction for heterogeneity causal effect estimation," In proceedings of the 24th IEEE International Conference on High Performance Computing & Communications (HPCC), 2022. (Oral) [pdf]

  42. H. Shu*, P. Gao, Z. Yang, C. Li, and M. Wu, "Exploring the feasibility of transformer-based models on question relatedness," In proceedings of the 24th IEEE International Conference on High Performance Computing & Communications (HPCC), 2022. (Oral) [pdf]

  43. H. Jia*, Z. Yang, P. Gao, M. Wu, C. Li, Y. Kan, and R. Zhang, "Automatic sleep staging via frequency-wise spiking neural networks," In proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022. (Oral) [pdf]

  44. Z. Yang*, L. Zhu, C. Li, Z. Chen, N. Ono, M.D. Altaf-Ul-Amin, and S. Kanaya, "Hierarchical categorical generative modeling for multi-omics cancer subtyping," In proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (BIBM), 2022. (Oral) [pdf]

  45. C. Li*, J. Zheng, H. Okamura, and T. Dohi, "Software reliability prediction through encoder-decoder recurrent neural networks," In proceedings of the Reliability and Maintenance Engineering Summit (RMES 2021), Nantong, China, 2021. (Oral) [pdf]

  46. C. Li* and J. Zheng, "API call-based malware classification using recurrent neural networks," In proceedings of the 4th International Conference on Mathematical Techniques in Engineering Applications (ICMTEA 2020), Uttarakhand, India, 2020. (Oral) [pdf]

  47. M. Qaosar*, K. Md. R. Alam, C. Li, and Y. Morimoto, "Privacy-preserving Top-k dominating queries in distributed multi-party databases," In proceedings of the 2019 IEEE International Conference on Big Data (Big Data), pp.5794-5803, Los Angeles, CA, USA, 2019. (Oral) [pdf]

  48. C. Li*, X. Zhang, M. Qaosar, S. Ahmed, K. Md. R. Alam, and Y. Morimoto, "Multi-factor-based stock price prediction using hybrid neural networks with attention mechanism," In proceedings of the 5th IEEE International Conference on Cloud and Big Data Computing (CBDCom 2019), pp.961-966, Fukuoka, Japan, 2019. (Oral) [pdf]

  49. M. Qaosar*, S. Ahmed, C. Li, and Y. Morimoto, "Hybrid sensing and wearable smart device for health monitoring and medication: opportunities and challenges," In proceedings of the 2018 AAAI Spring Symposium Series, pp.269-274, March 2018. (Oral) [pdf]

  50. C. Li*, M. He, M. Qaosar, S. Ahmed, and Y. Morimoto, "Capturing temporal dynamics of users’ preference from purchase history big data for recommendation system," In proceedings of the 2018 IEEE International Conference on Big Data (Big Data), pp.5372-5374, Seattle, WA, USA, 2018. (Oral) [pdf]

  51. C. Li*, Annisa, A. Zaman, and Y. Morimoto, "MapReduce-based computation of area skyline query for selecting good locations in a map," In proceedings of 2017 IEEE International Conference on Big Data (Big Data), pp.4779-4782, Boston, MA, USA, 2017. (Oral) [pdf]

    52. Local Conferences in Japan

  52. 難波里子, 李晨,大谷則子, 山西芳裕, "遺伝子摂動応答トランスクリプトームを用いた半教師あり深層学習による治療標的分子の予測と希少疾患への応用（Prediction of therapeutic target molecules for rare diseases using gene perturbation transcriptome and semi-supervised deep learning）," 第130回 日本解剖学会／第102回 日本生理学会／第98回 日本薬理学会合同大会 [The 130th Annual Meeting of the Japanese Association of Anatomists (JAA)・The 102nd Annual Meeting of the Physiological Society of Japan (PSJ)・The 98th Annual Meeting of the Japanese Pharmacological Society (JPS)], 一般口頭発表, 千葉, 2025年３月18日.

  53. 難波里子, 李晨,大谷則子, 山西芳裕, "深層学習による遺伝子摂動応答トランスクリプトームを用いた創薬標的分子の予測と希少疾患への応用（Predicting therapeutic target molecules for rare diseases based on deep learning and genetically perturbed transcriptome）," 日本薬学会第145回（The 145th Annual Meeting of the Pharmaceutical Society of Japan）, 一般口頭発表, 福岡, 2025年３月28日.

  54. S. Namba*, C. Li, N. Otani, and Y. Yamanishi, "機械学習による治療標的分子の予測と希少疾患への応用," 第10回CBI学会個別化医療研究会, Gifu, Japan, 2024. (Oral) [pdf]

  55. K. Yasuda*, C. Li, K. Kaitoh, and Y. Yamanishi, "複数の治療標的分子にデュアルで作用する医薬品化合物を構造生成するAI開発," The 46th Chemoinformatics Meeting, Tokyo, Japan, 2023. (Oral) [pdf]

  56. C. Li* and Y. Yamanishi, "Scaffold-retained transformer GAN for molecular generation with chemical property optimization," Chem-Bio Informatics Society (CBI) Annual Meeting, Tokyo, Japan, 2023. (Oral) [pdf]

  57. K. Yasuda*, C. Li, K. Kaitoh, and Y. Yamanishi, "Transformer encoder-based generative adversarial network for design of polypharmacological drugs," Chem-Bio Informatics Society (CBI) Annual Meeting, Tokyo, Japan, 2023. (Oral) [pdf]

  58. C. Li*, Zheng, H. Okamura, and T. Dohi, "A note on Markovian arrival process parameter estimation of quasi-birth-death queueing systems with utilization data," Queueing symposium 2023, Waseda University, Japan, 2023. (Oral) [pdf]

  59. Y. Matsukiyo, C. Yamanaka, C. Li, and Y. Yamanishi*, "Gene expression data-driven scaffold-constrained molecular structure generation by deep neural network," The 8th Autumn School of Chemoinformatics in Nara 2023 (HP), Nara, 2023. [pdf]

  60. C. Li* and Y. Yamanishi, "Molecular generation using sequence-based transformer generative adversarial network," Chem-Bio Informatics Society (CBI) Annual Meeting, Tokyo, Japan, 2022. (Oral) [pdf]

  61. C. Li* and J. Zheng, "A note on transformer encoder-based malware classification using API calls," IEICE Tech. Rep., 2022. (Oral) [pdf]

  62. C. Li*, K. Kaitoh, and Y. Yamanishi, "Transformer-based generative adversarial networks for generating molecules with desired properties," Chem-Bio Informatics Society (CBI) Annual Meeting, Tokyo, Japan, 2021. (Oral) [pdf]

  63. C. Li*, J. Zheng, H. Okamura, and T. Dohi, "A note on performance evaluation of cloud datacenters using CPU utilization data," IEICE Tech. Rep., vol. 121, no. 276, R2021-34, pp. 1-6, November 2021. (Oral) [pdf]

  64. C. Li*, C. Luo, H. Okamura, and T. Dohi, "A note on performance evaluation of system with CPU utilization data," IEICE-Assurance System conference, Hiroshima, Japan, 2015. (Oral) [pdf]

    65. Others

  65. C. Li, H. Tang, Y. Zhu, Y. Yamanishi, "A reinforcement learning-driven transformer GAN for molecular generation," arXiv:2503.12796, March 2025. (Preprint) [pdf]

  66. C. Li, D. Cheng, Y. Morimoto, "Leveraging deep neural networks for aspect-based sentiment classification," arXiv:2503.12803, March 2025. (Preprint) [pdf]

  67. C. Li, "Transformer GANを用いた化学特性の最適化を目指した分子生成 [in Japanese] (Transformer GAN-based molecular generation with property optimization [in English])," Invited Talk at the 455th Chem-Bio Informatics Society (CBI) Annual Meeting, Tokyo, Japan, May 2024. [pdf]

  68. Y. Zhu, G. Li, C. Li, and Y. Cao, "Moving beyond traditional anomaly detection," Tutoial in the 27th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2023), Osaka, Japan, 2023. (Peer Reviewed・Oral) [pdf]

Note: * indicates the corresponding author.