| Title | MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network |
| Creator | |
| Date Issued | 2025-02-01 |
| Source Publication | Bioinformatics
 |
| ISSN | 1367-4803 |
| Volume | 41Issue:2 |
| Abstract | Motivation: The increasing accessibility of large-scale protein sequences through advanced sequencing technologies has necessitated the development of efficient and accurate methods for predicting protein function. Computational prediction models have emerged as a promising solution to expedite the annotation process. However, despite making significant progress in protein research, graph neural networks face challenges in capturing long-range structural correlations and identifying critical residues in protein graphs. Furthermore, existing models have limitations in effectively predicting the function of newly sequenced proteins that are not included in protein interaction networks. This highlights the need for novel approaches integrating protein structure and sequence data. Results: We introduce Multi-scalE Graph Adaptive neural network (MEGA-GO), highlighting the capability of capturing diverse protein sequence length features from multiple scales. The unique graph adaptive neural network architecture of MEGA-GO enables a more nuanced extraction of graph structure features, effectively capturing intricate relationships within biological data. Experimental results demonstrate that MEGA-GO outperforms mainstream protein function prediction models in the accuracy of Gene Ontology term classification, yielding 33.4%, 68.9%, and 44.6% of area under the precision-recall curve on biological process, molecular function, and cellular component domains, respectively. The rest of the experimental results reveal that our model consistently surpasses the state-of-the-art methods. |
| DOI | 10.1093/bioinformatics/btaf032 |
| URL | View source |
| Language | 英语English |
| Scopus ID | 2-s2.0-85217805358 |
| Citation statistics | |
| Document Type | Journal article |
| Identifier | http://repository.uic.edu.cn/handle/39GCC9TT/12511 |
| Collection | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author | Chen，Jiaxing |
| Affiliation | 1.Guangdong Provincial Key Laboratory IRADS,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,519087,China 2.Department of Computer Science,Beijing Normal University-Hong Kong Baptist University United International College,Zhuhai,519087,China 3.Department of Computer Science and Technology,Guangdong University of Technology,Guangzhou,510520,China 4.Department of Science of Chinese Materia Medica,Guangdong Medical University,Dongguan,524023,China 5.Department of Computer Science,City University of Hong Kong,Hong Kong |
| First Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Corresponding Author Affilication | Beijing Normal-Hong Kong Baptist University |
| Recommended Citation GB/T 7714 | Lee，Yujian,Gao，Peng,Xu，Yongqiet al. MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network[J]. Bioinformatics, 2025, 41(2). |
| APA | Lee，Yujian, Gao，Peng, Xu，Yongqi, Wang，Ziyang, Li，Shuaicheng, & Chen，Jiaxing. (2025). MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network. Bioinformatics, 41(2). |
| MLA | Lee，Yujian,et al."MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network". Bioinformatics 41.2(2025). |
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