辛坤鎰 - 助理教授
辛坤鎰 助理教授
專任教師
動物生產組
(04)22840365#218
(04)22860265
看運氣、看緣分、看心情
結構與系統生物學、生物資料處理與分析、機械學習技術之應用
英國愛丁堡大學博士
經歷
2018-PRES  專任助理教授,國立中興大學動物科學系
2018-2019  Visiting Researcher, Okinawa Institute of Science and Technology Graduate University(OIST), Japan.
2017-PRES  兼任助理教授,國立台灣大學動物科學技術學系
2014-2018  Staff Scientist, Okinawa Institute of Science and Technology Graduate University(OIST), Japan.
2010-2014 Postdoctoral, Okinawa Institute of Science and Technology Graduate University(OIST), Japan.
2009-2010 Postdoctoral Associate, University of Edinburgh, UK.
2001-2004  約聘助理,行政院農委會畜牧處
2000-2001  研究助理,財團法人台灣動物科技研究所
 
期刊論文
  • Lin, G.Y., Su, Y. C., LinHuang Y., Hsin, K. Y.§ (2023) MESPEUS: a database of metal coordination groups in proteins. Nucleic Acids Research. DOI: https://doi.org/10.1093/nar/gkad1009
  • 蘇禹丞, 吳佩璇, 陳佳萱, 辛坤鎰§. (2023) 應用機械學習技術預測母豬第三胎出生活仔數。農林學報。
  • 辛坤鎰, 黃三元, 陳姿伶§, 凃又方. (2022) 臺灣養鹿產業經營現況與鹿茸生產成本分析研究。農林學報 69(4)。DOI: 10.30089/JAF.202212_69(4).0006
  • Hsiao, W. C., Hsin, K. Y.*, Wu, Z. W., Song, J. S., Yeh, Y. N., Chen, Y. F., Tsai, C. H., Chen, P. H., Shia, K. S., Chang, C. P., Hung. M. S. (2023)  Modulating the affinity and signaling bias of cannabinoid receptor 1 antagonists. Bioorganic Chemistry 130. DOI: https://doi.org/10.1016/j.bioorg.2022.106236
  • Hsin, K. Y. (2020) Computational Modelling of Nanomaterials, 1st Edition. Chapter 6 P77-P93, Protein Modeling. Vol 17, ISBN 9780128214954. Elsevier.
  • Yeh, Y. N., Hsin, K. Y., Zimmer, A., Lin, L. Y.,  Hung, M. S. (2019) A structure-function approach identifies L-PGDS as a mediator responsible for glucocorticoid-induced leptin expression in adipocytes. Biochemical Pharmacology 166, 203–211. DOI: https://doi.org/10.1016/j.bcp.2019.05.022.
  • Chiba, S., M. Ohue, A. Gryniukova, P. Borysko, S. Zozulya, N. Yasuo, R. Yoshino, K. Ikeda, W. Shin, D. Kihara, M. Iwadate, H. Umeyama, T. Ichikawa, R. Teramoto, K. Y. Hsin, V. Gupta, H. Kitano, and M. Sakamoto. (2019) A prospective compound screening contest identified broader inhibitors for Sirtuin 1. Scientific Reports 9(1):19585-19585. DOI.org/10.1038/s41598-019-55069-y
  • 辛坤鎰 (2018) 機械學習於農業之應用 - 以動物及其產品生產與管理為例。台灣農學會報18(3-4):275-290。DOI:10.6730/JAAT.201709-12_18(3-4).0001
  • 辛坤鎰*, 林美峰. (2017) 常見之球蟲藥及其使用規範。中畜會誌 46(4) 295-310。
  • Hsin, K. Y.*§, Matsuoka, Y., Asai, Y., Kyota. K., Watanabe, T., Kawaoka, Y., Kitano, H. (2016) systemsDock: a web server for network pharmacology-based prediction and analysis. Nucleic Acids Research. 44 (W1): W507-W513. DOI: 10.1093/nar/gkw335.
  • Chiba, S., Ikeda, K., Ishida, T., Gromiha, M.M., Taguchi, Y., Iwadate, M., Umeyama, H., Hsin, K. Y., Kitano, H. and Yamamoto, K. (2015) Identification of potential inhibitors based on compound proposal contest: Tyrosine-protein kinase Yes as a target. Scientific Reports, 5. DOI: 10.1038/srep17209.
  • Harding M. M. and Hsin, K. Y. (2014) Mespeus: A Database of Metal Interactions with Proteins. Chapter 23 P333-P342, Structural Genomics: General Applications, Methods in Molecular Biology, vol. 1091, DOI 10.1007/978-1-62703-691-7_23, Springer Science Business Media, LLC 2014.
  • Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K. Y., Kitano, H. (2013) Toward an integrated software platform for systems pharmacology. Biopharmaceutics & Drug Disposition. 34(9):508-26. DOI: 10.1002/bdd.1875.
  • Hsin, K. Y.* §, Ghosh, S., Kitano, H. (2013) Combining machine learning systems and multiple docking simulation packages to improve docking prediction reliability for network pharmacology. PLOS ONE. DOI: 10.1371/journal.pone.0083922.
  • Ghosh, S., Matsuoka, Y., Asai, Y., Hsin, K. Y., Kitano, H. (2011) Software for systems biology: from tools to integrated platforms. Nature Reviews Genetics 12, 821-832.
  • Hsin, K. Y.*, H. P. Morgan, S. Shave, A. Hinton, P. Taylor and M. D. Walkinshaw (2011). EDULISS: a small-molecule database with data-mining and pharmacophore searching capabilities. Nucleic Acids Research 39: D1042-D1048.
  • Morgan, H.P., McNae, I.W., Hsin, K. Y., Michels, P.A., Fothergill-Gilmore, L.A. and Walkinshaw, M.D. (2010). An improved strategy for the crystallization of Leishmania mexicana pyruvate kinase. Acta crystallographica. Section F, Structural biology and crystallization communications 66: 215-218.
  • Hsin, K. Y.*, Y Sheng, MM Harding, P Taylor, MD Walkinshaw (2008). MESPEUS: a database of the geometry of metal sites in proteins. Journal of Applied Crystallography 41(5): 963-968
  • Taylor, P., Blackburn E., Sheng, YG., Harding S, Hsin, K. Y., Kan, D., Shave, S., and Walkinshaw, MD. (2008). Ligand discovery and virtual screening using the program LIDAEUS. British Journal of Pharmacology 153: S55-S67.
 
研討會論文
  • Lin, G. Y., Hsin, K. Y.§ (2023) Study of characteristics for the metal sites in metalloprotein. Journal of Chinese Society of Animal Science 52.
  • Lin-Huang, Y., Wu, P. H., Luu, K. N., Hsin, K. Y.§ (2023) PatherDB 3.0: A network pharmacology-based database for molecular bioactivity investigation. Journal of Chinese Society of Animal Science 52.
  • Lin, G. Y., Wu, P. H., Hsin, K. Y.§ (2023) Study of metal-protein interactions. 2023 RIKEN-NCHU Joint Symposium.
  • Su, Y. C., Lin-Huang, Y., Hsin, K. Y.§ (2023) PatherDB 1.2: A molecular bio-activity database for network pharmacology-based study. 2023 RIKEN-NCHU Joint Symposium.
  • Lin, G. Y., Su, Y. C., Liao, W. N., Lin-Huang, Y., Hsin, K. Y.§ (2023) PatherDB: A network pharmacology-based database for molecular bioactivity investigation. The 10th International Congress of Asian Society of Toxicology.
  • Liao, W. N., Hsin, K. Y.§ (2022) Application of machine learning technology to predict protein-protein interaction. Journal of Chinese Society of Animal Science 51 (suppl.): 233.
  • Lin, G. Y., Hsin, K. Y.§ (2022) Study of metal coordination groups in metalloprotein. Journal of Chinese Society of Animal Science 51 (suppl.): 227.
  • Su, Y. C., Hsin, K. Y.§ (2022) Application of data science technology to predict reproductive performance in sows. Journal of Chinese Society of Animal Science 51 (suppl.): 109.
  • Su, Y. C., Chen, C. H., Liao, W. N., Lin, G. Y., Huang, Y. L., Hsin, K. Y.§ (2022) Application of machine learning technology to predict reproductive performance in sows. The 6th Fatty Pig International Conference.
  • Hsin, K. Y.*§, Kitano, H., Matsuoka, Y. and Ghosh, S. (2015), Application of machine leaning approaches in drug target identification and network pharmacology. IEEE conference proceeding, pp. 219-219. DOI: 10.1109/ICIIBMS.2015.7439493.
 
專利技術
  • Interaction prediction device, interaction prediction method, and computer program product.(14/407,835).
  • Prediction program, method and device for interaction between a small molecule and targeted protein. (JP2012-134261)
 
歷年執行計畫
  • 2023 開發實驗動物減量替代之智慧平台 (農委會科技計畫)
  • 2023 開發畜禽產品原產地分析技術及機械學習鑑別模型 (農委會科技計畫)
  • 2022 開發實驗動物減量替代之智慧平台 (農委會科技計畫)
  • 2022 應用影像處理及機械學習技術觀測母豬哺乳行為 (農委會科技計畫)
  • 2022 建置機械學習模型及雲端系統進行雞肉產地辨識 (中央畜產會)
  • 2022 強化豬肉產地辨識機械學習模型之預測效能 (中央畜產會)
  • 2021 開發實驗動物減量替代之智慧平台 (農委會科技計畫)
  • 2021 應用資料科學技術預測母豬繁殖性能 (農委會科技計畫)
  • 2021 應用機械學習技術預測蛋白質相互作用及建構分子路徑 (國科會計畫)
  • 2021 應用資料科學技術進行豬肉重金屬及同位素資料之特徵值分析 (中央畜產會)
  • 2021 建置雞肉產地鑑別機械學習模型與線上分析平台 (中央畜產會)
  • 2020 開發實驗動物減量替代之智慧平台 (農委會科技計畫)
  • 2020 應用機械學習技術預測豬隻生長性能 (農委會科技計畫)
  • 2020 開發機械學習方法進行豬肉同位素資料特徵值之分析 (中央畜產會)
  • 2020 開發機械學習技術及雲端平台辨識雞肉產地 (中央畜產會)
  • 2018  OIST Incentive Program-Support for KAKENHI grant applications.
  • 2017  OIST Incentive Program-Support for KAKENHI grant applications.
  • 2014  JSPS KAKENHI, Japan (Two years; Ref. 26730152)
  • 2014  IMSUT Joint Research Project, The Institute of Medical Science, The University Of Tokyo (Ref. 2014-343)
 
其他
Research Interests:
農業智慧化、生物系統電腦模擬模式、分子結構模擬與接合親和力預測、分子代謝及訊息路徑分析、藥物研發