| How Many 3D Structures Do We Need to Train a Predictor？ |
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| 作者姓名： | Pantelis G. Bagos Georgios N. Tsaousis Stavros J. Hamodrakas |
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| 作者单位： | Pantelis G. Bagos(Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 15701, Greece;Department of Computer Science and Biomedical Informatics, University of Central Greece, Lamia 35100, Greece);Georgios N. Tsaousis,Stavros J. Hamodrakas(Department of Cell Biology and Biophysics, Faculty of Biology, University of Athens, Athens 15701, Greece) |
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| 基金项目： | PGB was supported by a scholarship from the State Scholarships Foundation of Greece (SSF) for postdoctoral research in the Department of Cell Biology and Biophysics of the University of Athens (Machine Learning Algorithms for Bioinformatics) |
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| 摘 要： | It has been shown that the progress in the determination of membrane protein structure grows exponentially, with approximately the same growth rate as that of the water-soluble proteins. In order to investigate the effect of this, on the performance of prediction algorithms for both α-helical and β-barrel membrane proteins, we conducted a prospective study based on historical records. We trained separate hidden Markov models with different sized training sets and evaluated their performance on topology pred...
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| 关 键 词： | 预测算法 三维结构 二级结构预测 跨膜蛋白质 隐马尔可夫模型 培养 同源序列 α-螺旋 |
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