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    Showing items 6076-6100 of 11653. (467 Page(s) Totally)
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    DateTitleAuthors
    2011-04 A Practical U-Healthcare Network with Security and QoS 黃秀園; Huang, Shiow-Yuan
    Precise Image Unwarping of Omnidirectional Cameras with Hyperbolic-Shaped Mirrors S. W. Jeng; W. H. Tsai
    1994-12 Preclassification of Chinese characters by hierarchical neural networks Y. S. Chen; W. H. Tsai
    2011-02 The precoder design for intrablock MMSE equalization and block delay detection with a modified oblique projection framework 吳俊賢; Wu, Chun-Hsien
    2004-12 Predication of Gene Regulatory Networks using Differential Expression of cDNA Microarray Data 蔡進發; Jeffrey, J.P.Tsai
    2004-12 Predication of Gene Regulatory Networks using Differential Expression of cDNA Microarray Data K.C.Shih; K.C.Shih; R.M.Chen; R.M.Chen; 蔡進發; Jeffrey, J.P.Tsai
    2010 Predicting Algorithm of Thermostable Proteins Based on Choquet Integral with Respect to L-measure and Hurst Exponent Liu Yi Cheng
    2006-10 Predicting Cancerous Genes based on Regular Truth Table Jeffrey J. P. Tsai; R. M. Chen; K.C. Shih; R.M. Hsu
    2009 Predicting Protein Methylation Sites with Physicochemical Properties Tsung-Ying Tsai
    2000 Predicting Regulatory Elements in Repetitive Sequences Using Transcription Factor Binding Sites J.T. Horng; Wen-Fu Cho
    2008-09 Predicting Subcellular Location of Eukaryotic Proteins using Baysian and K-Nearest Neighbor Classifier Jeffrey J. P. Tsai; H. W. Hsiao; S. H. Chen; P.C. Chang
    2008 Predicting subcellular locations of eukaryotic proteins using bayesian and k-nearest neighbor classifiers Hsiao, Han C. W.; Chen, Shih-Hao; Chang, Judson Pei-Chun; Tsai, Jeffrey J. P.
    2008 Predicting subcellular locations of eukaryotic proteins using bayesian and k-nearest neighbor classifiers 蕭震緯; Hsiao, Chen-Wei; 張培均; Chang, Pei-Chun; 蔡進發; Jeffrey, J.P.Tsai
    2008-09 Predicting subcellular locations of eukaryotic proteins using Bayesian and k-nearest neighbor classifiers Han C.W. Hsiao; S.H. Chen; P.C. Chang; Jeffrey J.P. Tsai
    2007 Predicting subcellular locations of eukaryotic proteins using bayesian and k-nearest neighbor classifiers Han-Wen Hsiao; Shih-Hao Chen; Pei-Chun Chang; Jeffery J.P. Tsai
    2008-09 Predicting subcellular locations of eukaryotic proteins using Bayesian and k-nearest neighbor classifiers Hsiao, HCW (Hsiao, Han C. W.); Chen, SH (Chen, Shih-Hao); Chang, JPC (Chang, Judson Pei-Chun); Tsai, JJP (Tsai, Jeffrey J. P.)
    2017 Predicting the Blood Pressure via Electrocardiogram Signal ZHONG, LIN-YOU
    2007-12-20 Predicting the function of hydrolase proteins based on the recognition of motifs 曾怜玉; 王冠棋; 蔡杰松
    2006 Prediction and classification of thermostable proteins by using Hidden Markov Model Hsing-Yuan Tseng
    Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms Chien-Hung H(Chien-Hung Huang)、Huai-Shun Pe(Huai-Shun Peng)、吳家樂(Ng, Ka-Lok)*
    2015-03 Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms Chien-Hung H;Chien-Hung Huang;Huai-Shun Pe;Huai-Shun Peng;吳家樂;Ng, Ka-Lok
    2015-03 Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms Chien-Hung, H; Huang, Chien-Hung; Pe, Huai-Shun; Peng, Huai-Shun; 吳家樂; Ng, Ka-Lok
    2015-03 Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms Chien-Hung, H; Huang, Chien-Hung; Pe, Huai-Shun; Peng, Huai-Shun; 吳家樂; Ng, Ka-Lok
    2015-03 Prediction of Cancer Proteins by Integrating Protein Interaction, Domain Frequency, and Domain Interaction Data Using Machine Learning Algorithms Huang), Chien-Hung H(Chien-Hung; Peng), Huai-Shun Pe(Huai-Shun; 吳家樂(Ng, Ka-Lok)*
    2005 Prediction of Cancerous Genes using cDNA Microarray Data and Gene Regulation Relationship K.C. Shih

    Showing items 6076-6100 of 11653. (467 Page(s) Totally)
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