DeepMFFGO: A Protein Function Prediction Method for Large-Scale Multifeature Fusion.

IF 5.3 2区化学 Q1 CHEMISTRY, MEDICINAL

Journal of Chemical Information and Modeling Pub Date : 2025-04-14 Epub Date: 2025-03-21 DOI:10.1021/acs.jcim.5c00062

Jingfu Wang, Jiaying Chen, Yue Hu, Chaolin Song, Xinhui Li, Yurong Qian, Lei Deng

引用次数: 0

Abstract

Protein functional studies are crucial in the fields of drug target discovery and drug design. However, the existing methods have significant bottlenecks in utilizing multisource data fusion and Gene Ontology (GO) hierarchy. To this end, this study innovatively proposes the DeepMFFGO model designed for protein function prediction under large-scale multifeature fusion. A fine-tuning strategy using intermediate-level feature selection is proposed to reduce redundancy in protein sequences and mitigate distortion of the top-level features. A hierarchical progressive fusion structure is designed to explore feature connections, optimize complementarity through dynamic weight allocation, and reduce redundant interference. On the CAFA3 data set, the F_max values of the DeepMFFGO model on the MF, BP, and CC ontologies reach 0.702, 0.599, and 0.704, respectively, which are improved by 4.2%, 2.4%, and 0.07%, respectively, compared with state-of-the-art multisource methods.

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DeepMFFGO：一种大规模多特征融合的蛋白质功能预测方法。

蛋白质功能研究在药物靶点发现和药物设计领域至关重要。然而，现有的方法在利用多源数据融合和基因本体（GO）层次结构方面存在很大的瓶颈。为此，本研究创新性地提出了大规模多特征融合下蛋白质功能预测的DeepMFFGO模型。提出了一种利用中间层次特征选择的微调策略，以减少蛋白质序列的冗余，减轻顶层特征的失真。设计了一种分层递进融合结构，探索特征连接，通过动态权重分配优化互补性，减少冗余干扰。在CAFA3数据集上，DeepMFFGO模型在MF、BP和CC本体上的Fmax值分别达到0.702、0.599和0.704，比目前最先进的多源方法分别提高4.2%、2.4%和0.07%。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of Chemical Information and Modeling 化学-化学综合

CiteScore

9.80

自引率

10.70%

发文量

529

审稿时长

1.4 months

期刊介绍： The Journal of Chemical Information and Modeling publishes papers reporting new methodology and/or important applications in the fields of chemical informatics and molecular modeling. Specific topics include the representation and computer-based searching of chemical databases, molecular modeling, computer-aided molecular design of new materials, catalysts, or ligands, development of new computational methods or efficient algorithms for chemical software, and biopharmaceutical chemistry including analyses of biological activity and other issues related to drug discovery. Astute chemists, computer scientists, and information specialists look to this monthly’s insightful research studies, programming innovations, and software reviews to keep current with advances in this integral, multidisciplinary field. As a subscriber you’ll stay abreast of database search systems, use of graph theory in chemical problems, substructure search systems, pattern recognition and clustering, analysis of chemical and physical data, molecular modeling, graphics and natural language interfaces, bibliometric and citation analysis, and synthesis design and reactions databases.