中圖分類(lèi)號(hào)：TP399 文獻(xiàn)標(biāo)志碼：A DOI: 10.16157/j.issn.0258-7998.234794
中文引用格式： 古海博，王成鳳，金遠(yuǎn)，等. 融合蛋白質(zhì)語(yǔ)言模型與深度神經(jīng)網(wǎng)絡(luò)的植物蛋白質(zhì)相互作用預(yù)測(cè)研究[J]. 電子技術(shù)應(yīng)用，2024，50(4)：22-28.
英文引用格式： Gu Haibo，Wang Chengfeng，Jin Yuan，et al. Prediction of plant protein-protein interaction based on fusion of protein language model and deep neural network[J]. Application of Electronic Technique，2024，50(4)：22-28.

Prediction of plant protein-protein interaction based on fusion of protein language model and deep neural network

Abstract： Predicting protein-protein interaction (PPI) in plants holds significant biological implications. This study has employed four encoding methods and a deep neural network to construct a model for predicting protein interactions. The results show that the developed PPI prediction model using the integrated approach of the protein language model Ankh with a deep neural network has achieved optimal AUPR and AUC values across three plant datasets, with its Sen and MCC values also outperforming those of four other models designed for protein interaction predictions. When tested on plant PPI datasets for rice and soybean, the proposed model has yielded AUPR scores of 0.802 5 and 0.730 1 respectively, and AUC scores of 0.956 2 and 0.950 7 respectively. These outstanding results indicate that the PPI model incorporating the protein language model Ankh can serve as a promising tool for predicting protein-protein interactions in plants.

Key words : plant protein-protein interation；protein language model；deep neural network