Project 5: Data mining and Chinese medicine Abstract By using data mining techniques, we are ¢ Searching active ingredients (herbs or chemical compounds) in the effective prescriptions of Chinese medicine for specific diseases; ¢ Identifying Chinese medicine material by their "fingerprints" detected by modem analytical instruments and ¢ identifying the quality criteria of Chinese medicinal products. Keywords: Chinese medicine Organization: Hong Kong Baptist University, Chinese Medicine Informatics Centre, Institute for the Advancement of Chinese Medicine Primary Contact: Josef Siu-wai Leung Address: Institute for the Advancement of Chinese Medicine, Hong Kong Baptist University, Kowloon Tong, Hong Kong Email: j.leung@ieee.org URL: http://www.hkbu, edu.hk/~iacm Duration: 0.5 year Number of People: 2 Tools Used: C5, HCV, Progol and commercial neural network shells Academic Disciplines: Chinese medicine Project 6: Drug design, protein secondary structure prediction and functional genomics Organization: University of Wales, Aberystwyth, Abstract Bioinformatics and Biocomputation, Department of Computer Science Inductive Logic Programming (ILP), Model Based Primary Contact: Dr. Ross D. King Reasoning, Evolutionary computing, Artificial neural Address: Department of Computer Science networks, Multivariate statistics. Drug Design, Protein The University of Wales, Aberystwyth Secondary Structure Prediction, Functional Genomics, Penglais, Aberystwyth, Ceredigion Spectral interpretation for process monitoring, titre SY23 3DB, Wales, U.K. improvement, and organism identification. Tel: 44 1970--622432 Fax: 44 1970-622455 Keywords: drug design, bioinformatics, functional Email: rdk@aber.ac.uk genomics URL: http://www,aber.ac.uk/.-dcswww/Research/bio/ Duration: 5 years Number of People: 7 Tools: Developed: Progol Purchased: Clementine Academic Disciplines: Computer Science, Molecular Biology Funding Sources: HEFC, EPSRC, BBSRC, and MRC Project Related Publications: King, R.D., Muggleton, S.H., Srinivasan, A., & Steinberg, M.J.E., "Structure Activity Relationships Derived by Machine Learning: The Use of Atoms and their Bond Connectivities to Predict Mutagenicity Using Inductive Logic Programming", Proceedingsof the NationalAcademy of Sciences U.S,4. 93, 438-442, 1996. Srinivasan, A., Steinberg, M.J.E., King, R.D., "Theories for Mutagenicity: A Study of First-Order and Feature Based Induction", ArtificialIntelligenceJournal, 85, 277-299, 1996.
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