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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >Intelligent Consensus Modeling for ProlineCis-Trans Isomerization Prediction
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Intelligent Consensus Modeling for ProlineCis-Trans Isomerization Prediction

机译：ProlineCis反式异构化预测的智能共识模型

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摘要

Proline cis-trans isomerization (CTI) plays a key role in the rate-determining steps of protein folding. Accurate prediction of proline CTI is of great importance for the understanding of protein folding, splicing, cell signaling, and transmembrane active transport in both the human body and animals. Our goal is to develop a state-of-the-art proline CTI predictor based on a biophysically motivated intelligent consensus modeling through the use of sequence information only (i.e., position specific scores generated by PSI-BLAST). The current computational proline CTI predictors reach about 70-73 percent Q2 accuracies and about 0.40 Matthew correlation coefficient (Mcc) through the use of sequence-based evolutionary information as well as predicted protein secondary structure information. However, our approach that utilizes a novel decision tree-based consensus model with a powerful randomized-metalearning technique has achieved 86.58 percent Q2 accuracy and 0.74 Mcc, on the same proline CTI data set, which is a better result than those of any existing computational proline CTI predictors reported in the literature.
机译：脯氨酸顺反异构化（CTI）在蛋白质折叠的速率确定步骤中起关键作用。脯氨酸CTI的准确预测对于了解蛋白质在人体和动物中的折叠，剪接，细胞信号传导和跨膜活性转运至关重要。我们的目标是仅通过使用序列信息（即由PSI-BLAST生成的位置特定分数），基于生物物理智能共识模型开发最新的脯氨酸CTI预测因子。当前的计算脯氨酸CTI预测因子通过使用基于序列的进化信息以及预测的蛋白质二级结构信息，可达到约70-73％的Q2准确度和约0.40的马修相关系数（Mcc）。但是，在相同的脯氨酸CTI数据集上，我们的方法利用新颖的基于决策树的共识模型和强大的随机金属学习技术，已达到86.58％的Q2准确度和0.74 Mcc，这比任何现有计算方法的结果都要好脯氨酸CTI预测因子在文献中已有报道。

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