Taha Kamal
Department of Computer Science, Khalifa University, Abu Dhabi, United Arab Emirates.
Comput Biol Med. 2025 Feb;185:109449. doi: 10.1016/j.compbiomed.2024.109449. Epub 2024 Dec 6.
This survey paper provides a comprehensive analysis of various Deep Learning (DL) techniques and algorithms for detecting protein-protein interactions (PPIs). It examines the scalability, interpretability, accuracy, and efficiency of each technique, offering a detailed empirical and experimental evaluation. Empirically, the techniques are assessed based on four key criteria, while experimentally, they are ranked by specific algorithms and broader methodological categories. Deep Neural Networks (DNNs) demonstrated high accuracy but faced limitations such as overfitting and low interpretability. Convolutional Neural Networks (CNNs) were highly efficient at extracting hierarchical features from biological sequences, while Generative Stochastic Networks (GSNs) excelled in handling uncertainty. Long Short-Term Memory (LSTM) networks effectively captured temporal dependencies within PPI sequences, though they presented scalability challenges. This paper concludes with insights into potential improvements and future directions for advancing DL techniques in PPI identification, highlighting areas where further optimization can enhance performance and applicability.
这篇综述论文全面分析了用于检测蛋白质-蛋白质相互作用（PPI）的各种深度学习（DL）技术和算法。它考察了每种技术的可扩展性、可解释性、准确性和效率，并提供了详细的实证和实验评估。从实证角度来看，这些技术是基于四个关键标准进行评估的；而从实验角度来说，它们是按照特定算法和更广泛的方法类别进行排名的。深度神经网络（DNN）表现出较高的准确性，但面临诸如过拟合和可解释性低等局限性。卷积神经网络（CNN）在从生物序列中提取分层特征方面效率很高，而生成随机网络（GSN）在处理不确定性方面表现出色。长短期记忆（LSTM）网络有效地捕捉了PPI序列中的时间依赖性，不过它们也存在可扩展性挑战。本文最后对改进DL技术以推进PPI识别的潜在改进和未来方向提出了见解，突出了进一步优化可提高性能和适用性的领域。
