A Distributed Framework for Large-scale Protein-protein Interaction Data Analysis and Prediction Using MapReduce

• Published in: IEEE/CAA journal of automatica sinica 2022-01, Vol.9 (1), p.160-172
• Main Authors: Hu, Lun, Yang, Shicheng, Luo, Xin, Yuan, Huaqiang, Sedraoui, Khaled, Zhou, MengChu
• Format: Article
• Language: English
• Subjects: Algorithms; Consumption; Data analysis; Data structures; Deep learning; Distributed computing; Efficiency; Genomics; large-scale prediction machine learning; MapReduce; Memory management; Noise reduction; Prediction algorithms; protein-protein interaction (PPI); Proteins
• ISSN: 2329-9266; 2329-9274
• DOI: 10.1109/JAS.2021.1004198

Protein-protein interactions are of great significance for human to understand the functional mechanisms of proteins. With the rapid development of high-throughput genomic technologies, massive protein-protein interaction (PPI) data have been generated, making it very difficult to analyze them efficiently. To address this problem, this paper presents a distributed framework by reimplementing one of state-of-the-art algorithms, i.e., CoFex, using MapReduce. To do so, an in-depth analysis of its limitations is conducted from the perspectives of efficiency and memory consumption when applying it for large-scale PPI data analysis and prediction. Respective solutions are then devised to overcome these limitations. In particular, we adopt a novel tree-based data structure to reduce the heavy memory consumption caused by the huge sequence information of proteins. After that, its procedure is modified by following the MapReduce framework to take the prediction task distributively. A series of extensive experiments have been conducted to evaluate the performance of our framework in terms of both efficiency and accuracy. Experimental results well demonstrate that the proposed framework can considerably improve its computational efficiency by more than two orders of magnitude while retaining the same high accuracy.