Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models
Robust Remote Homology Detection by Feature Based Profile Hidden Markov Models
复制标题
基于特征的轮廓隐马尔可夫模型的鲁棒远程同源检测
DOI：
10.2202/1544-6115.1159
复制
发表时间：
2005
影响因子：
0.9
通讯作者：
G. Fink
中科院分区：
文献类型：
--
作者：
T. Plötz;G. Fink
远程同源性的检测对于药物发现等分子生物学应用具有重要意义。即使对于最先进的蛋白质家族概率模型，即Profile hmm，这个问题仍然非常具有挑战性。为了改进远程同源性检测，提出了基于特征的半连续轮廓hmm。基于更丰富的序列表示，包括捕获残基在其局部环境中的生化特性的特征，家族特定的半连续模型是完全数据驱动的估计。此外，为了大大减少错误预测的数量，估计了一个明确的拒绝模型。对族特定半连续剖面HMM和非目标模型进行了竞争性评价。在基于超家族筛选SCOP数据库的实验评估中，我们证明了半连续剖面hmm显着优于其离散对应物。利用拒绝模型可以大大减少假阳性预测的数量，这是目标识别应用的重要前提。
The detection of remote homologies is of major importance for molecular biology applications like drug discovery. The problem is still very challenging even for state-of-the-art probabilistic models of protein families, namely Profile HMMs. In order to improve remote homology detection we propose feature based semi-continuous Profile HMMs. Based on a richer sequence representation consisting of features which capture the biochemical properties of residues in their local context, family specific semi-continuous models are estimated completely data-driven. Additionally, for substantially reducing the number of false predictions an explicit rejection model is estimated. Both the family specific semi-continuous Profile HMM and the non-target model are competitively evaluated. In the experimental evaluation of superfamily based screening of the SCOP database we demonstrate that semi-continuous Profile HMMs significantly outperform their discrete counterparts. Using the rejection model the number of false positive predictions could be reduced substantially which is an important prerequisite for target identification applications.
影响因子：
5.6
作者：
KROGH, A;BROWN, M;HAUSSLER, D
通讯作者：
HAUSSLER, D
DOI：
--
发表时间：
1993-07
期刊：
Proceedings. International Conference on Intelligent Systems for Molecular Biology
影响因子：
--
作者：
Michael Brown;Comput Er Science;R. Hughey;A. Krogh;I. Mian;Kimmen Sjslander;D. Haussler
通讯作者：
Michael Brown;Comput Er Science;R. Hughey;A. Krogh;I. Mian;Kimmen Sjslander;D. Haussler