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首页> 外文会议>2013 IEEE Workshop on Memetic Computing >Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor
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Prediction of protein-protein interactions from amino acid sequences using extreme learning machine combined with auto covariance descriptor

机译：使用极限学习机结合自协方差描述符从氨基酸序列预测蛋白质间的相互作用

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Protein-protein interactions (PPIs) are crucial for almost all cellular processes, including metabolic cycles, DNA transcription and replication, and signaling cascades. Unfortunately, the experimental methods for identifying PPIs are both time-consuming and expensive. Therefore, it is important to develop computational approaches for predicting PPIs. In this paper, a sequence-based method was developed for identifying new protein-protein interactions (PPIs) by means of Extreme Learning Machine (ELM) combined with a novel representation using auto covariance (AC). The AC descriptors account for the interactions between residues a certain distance apart in the protein sequence, thus this method adequately takes the neighboring effect into account and enables us to extract more PPI information from the protein sequences. ELM is a kind of accurate and fast-learning innovative classification method based on the random generation of the input-to-hidden-units weights followed by the resolution of the linear equations to obtain the hidden-tooutput weights. When performed on the PPI data of Saccharomyces cerevisiae, the proposed method achieved 90.42% prediction accuracy with 90.12% sensitivity at the precision of 90.67%. Extensive experiments are performed to compare our method with state-of-the-art techniques Support Vector Machine (SVM). Achieved results show that the proposed approach is very promising for predicting PPI, and would make a helpful supplement to experimental approaches.
机译：蛋白质-蛋白质相互作用（PPI）对于几乎所有细胞过程都至关重要，包括代谢循环，DNA转录和复制以及信号级联。不幸的是，用于识别PPI的实验方法既耗时又昂贵。因此，开发用于预测PPI的计算方法很重要。在本文中，开发了一种基于序列的方法，用于通过极限学习机（ELM）结合使用自协方差（AC）的新颖表示法来识别新的蛋白质-蛋白质相互作用（PPI）。 AC描述子解释了蛋白质序列中相距一定距离的残基之间的相互作用，因此该方法充分考虑了邻近效应，使我们能够从蛋白质序列中提取更多的PPI信息。 ELM是一种基于输入到隐藏单元权重的随机生成，然后解析线性方程组以获得隐藏到输出权重的，准确，快速学习的创新分类方法。当对酿酒酵母的PPI数据进行分析时，该方法的预测精度达到了90.42％，灵敏度达到了90.12％，精度达到了90.67％。进行了广泛的实验，以将我们的方法与最新技术支持向量机（SVM）进行比较。取得的结果表明，所提出的方法对于预测PPI很有前景，并将为实验方法提供有益的补充。

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