MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural network Article Swipe
Yujian Lee
,
Peng Gao
,
Yongqi Xu
,
Ziyang Wang
,
Shuai Cheng Li
,
Jiaxing Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf032
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bioinformatics/btaf032
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. Availability and implementation The source code and data of MEGA-GO are available at https://github.com/Cheliosoops/MEGA-GO.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1093/bioinformatics/btaf032
- https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf032/61591799/btaf032.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 40
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406739182
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4406739182Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bioinformatics/btaf032Digital Object Identifier
- Title
-
MEGA-GO: functions prediction of diverse protein sequence length using Multi-scalE Graph Adaptive neural networkWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-01-23Full publication date if available
- Authors
-
Yujian Lee, Peng Gao, Yongqi Xu, Ziyang Wang, Shuai Cheng Li, Jiaxing ChenList of authors in order
- Landing page
-
https://doi.org/10.1093/bioinformatics/btaf032Publisher landing page
- PDF URL
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf032/61591799/btaf032.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://academic.oup.com/bioinformatics/advance-article-pdf/doi/10.1093/bioinformatics/btaf032/61591799/btaf032.pdfDirect OA link when available
- Concepts
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Computer science, Protein function prediction, Graph, Source code, Data mining, Protein function, Artificial neural network, Artificial intelligence, Mega-, Machine learning, Theoretical computer science, Biology, Gene, Physics, Operating system, Biochemistry, AstronomyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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2Total citation count in OpenAlex
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2025: 2Per-year citation counts (last 5 years)
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40Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.yielding | 166 |
| abstract_inverted_index.available | 213 |
| abstract_inverted_index.capturing | 54, 113, 141 |
| abstract_inverted_index.component | 184 |
| abstract_inverted_index.efficient | 18 |
| abstract_inverted_index.features, | 139 |
| abstract_inverted_index.function, | 181 |
| abstract_inverted_index.function. | 25 |
| abstract_inverted_index.intricate | 142 |
| abstract_inverted_index.introduce | 102 |
| abstract_inverted_index.molecular | 180 |
| abstract_inverted_index.networks. | 86 |
| abstract_inverted_index.promising | 33 |
| abstract_inverted_index.research, | 47 |
| abstract_inverted_index.sequenced | 77 |
| abstract_inverted_index.sequences | 8 |
| abstract_inverted_index.structure | 96, 138 |
| abstract_inverted_index.surpasses | 198 |
| abstract_inverted_index.(MEGA-GO), | 108 |
| abstract_inverted_index.Motivation | 1 |
| abstract_inverted_index.annotation | 38 |
| abstract_inverted_index.approaches | 93 |
| abstract_inverted_index.biological | 145, 178 |
| abstract_inverted_index.capability | 111 |
| abstract_inverted_index.challenges | 52 |
| abstract_inverted_index.extraction | 135 |
| abstract_inverted_index.highlights | 88 |
| abstract_inverted_index.increasing | 3 |
| abstract_inverted_index.long-range | 55 |
| abstract_inverted_index.mainstream | 153 |
| abstract_inverted_index.predicting | 23, 72 |
| abstract_inverted_index.prediction | 27, 156 |
| abstract_inverted_index.sequencing | 11 |
| abstract_inverted_index.structural | 56 |
| abstract_inverted_index.Multi-scalE | 103 |
| abstract_inverted_index.demonstrate | 149 |
| abstract_inverted_index.development | 16 |
| abstract_inverted_index.effectively | 71, 140 |
| abstract_inverted_index.identifying | 59 |
| abstract_inverted_index.integrating | 94 |
| abstract_inverted_index.interaction | 85 |
| abstract_inverted_index.large-scale | 6 |
| abstract_inverted_index.limitations | 69 |
| abstract_inverted_index.outperforms | 152 |
| abstract_inverted_index.significant | 43 |
| abstract_inverted_index.Availability | 202 |
| abstract_inverted_index.Experimental | 147 |
| abstract_inverted_index.Furthermore, | 65 |
| abstract_inverted_index.architecture | 128 |
| abstract_inverted_index.consistently | 197 |
| abstract_inverted_index.correlations | 57 |
| abstract_inverted_index.experimental | 191 |
| abstract_inverted_index.highlighting | 109 |
| abstract_inverted_index.necessitated | 14 |
| abstract_inverted_index.technologies | 12 |
| abstract_inverted_index.Computational | 26 |
| abstract_inverted_index.accessibility | 4 |
| abstract_inverted_index.relationships | 143 |
| abstract_inverted_index.respectively. | 186 |
| abstract_inverted_index.implementation | 204 |
| abstract_inverted_index.classification, | 165 |
| abstract_inverted_index.precision-recall | 175 |
| abstract_inverted_index.state-of-the-art | 200 |
| abstract_inverted_index.https://github.com/Cheliosoops/MEGA-GO. | 215 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 6 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/9 |
| sustainable_development_goals[0].score | 0.4399999976158142 |
| sustainable_development_goals[0].display_name | Industry, innovation and infrastructure |
| citation_normalized_percentile.value | 0.86880067 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |