CPIELA: Computational Prediction of Plant Protein–Protein Interactions by Ensemble Learning Approach From Protein Sequences and Evolutionary Information - Li, Li-Ping; Zhang, Bo; Cheng, Li (Frontiers in Genetics, 2022)
Identification and characterization of plant protein-protein interactions (PPIs) is a critical step toward elucidating the functions of proteins and molecular mechanisms in plant cell. Although experimentally validated plant PPIs data have become increasingly available in diverse plant species, the high-throughput techniques are usually high cost and labor-intensive. With the incredibly valuable plant PPIs data accumulating in public databases, it is progressively important to propose computational approaches to facilitate the identification of possible PPIs. In this article, we propose an effective framework for predicting plant PPIs by combining Position-Specific-Scoring-Matrix (PSSM), Local Optimal Oriented Pattern (LOOP) and ensemble Rotation-Forest (ROF) model. Specifically, the plant protein sequence is firstly transformed into the PSSM, in which the protein evolutionary information is perfectly preserved. Then, the local textural descriptor LOOP is employed to extract texture variation features from PSSM. Finally, ROF classifier is adopted to infer the potential plant PPIs. The performance of CPIELA is evaluated via cross-validation on three plant PPIs datasets including Arabidopsis thaliana, Zea mays, and Oryza sativa datasets, respectively. The experimental results demonstrate that CPIELA method achieved the high average prediction accuracies of 98.63%, 98.09%, and 94.02%, respectively. To further verify the high performance of CPIELA, we also compared it with the other state-of-the-art methods on three gold standard datasets. The experimental results illustrate that CPIELA is efficient and reliable for predicting plant PPIs. It is anticipated that CPIELA approach could become a useful-tool-for facilitating the identification of possible plant PPIs.