IEEE Trans Cybern. 2017 Mar;47(3):731-743. doi: 10.1109/TCYB.2016.2524994. Epub 2016 Mar 30.
Protein-protein interactions (PPIs) play a central role in many biological processes. Although a large amount of human PPI data has been generated by high-throughput experimental techniques, they are very limited compared to the estimated 130 000 protein interactions in humans. Hence, automatic methods for human PPI-detection are highly desired. This work proposes a novel framework, i.e., Low-rank approximation-kernel Extreme Learning Machine (LELM), for detecting human PPI from a protein's primary sequences automatically. It has three main steps: 1) mapping each protein sequence into a matrix built on all kinds of adjacent amino acids; 2) applying the low-rank approximation model to the obtained matrix to solve its lowest rank representation, which reflects its true subspace structures; and 3) utilizing a powerful kernel extreme learning machine to predict the probability for PPI based on this lowest rank representation. Experimental results on a large-scale human PPI dataset demonstrate that the proposed LELM has significant advantages in accuracy and efficiency over the state-of-art approaches. Hence, this work establishes a new and effective way for the automatic detection of PPI.
蛋白质-蛋白质相互作用（PPIs）在许多生物过程中起着核心作用。尽管高通量实验技术已经产生了大量的人类 PPI 数据，但与人类估计的 130000 个蛋白质相互作用相比，这些数据非常有限。因此，人们非常希望能够自动检测人类 PPI。这项工作提出了一种新的框架，即低秩逼近核极限学习机（LELM），用于从蛋白质的一级序列中自动检测人类 PPI。它有三个主要步骤：1）将每个蛋白质序列映射到一个基于各种相邻氨基酸构建的矩阵上；2）应用低秩逼近模型对获得的矩阵进行求解，以获得其最低秩表示，从而反映其真实的子空间结构；3）利用强大的核极限学习机基于此最低秩表示来预测 PPI 的概率。在一个大规模的人类 PPI 数据集上的实验结果表明，与最先进的方法相比，所提出的 LELM 在准确性和效率方面具有显著的优势。因此，这项工作为 PPI 的自动检测建立了一种新的有效方法。
