Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review
Protein function prediction is crucial in several key areas such as bioinformatics and drug design. With the rapid progress of deep learning technology, applying protein language models has become a research focus. These models utilize the increasing amount of large-scale protein sequence data to de...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Bioengineering and Biotechnology |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1506508/full |
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| author | Jia-Ying Chen Jia-Ying Chen Jia-Ying Chen Jing-Fu Wang Jing-Fu Wang Jing-Fu Wang Yue Hu Yue Hu Yue Hu Xin-Hui Li Xin-Hui Li Xin-Hui Li Yu-Rong Qian Yu-Rong Qian Yu-Rong Qian Chao-Lin Song Chao-Lin Song Chao-Lin Song |
| author_facet | Jia-Ying Chen Jia-Ying Chen Jia-Ying Chen Jing-Fu Wang Jing-Fu Wang Jing-Fu Wang Yue Hu Yue Hu Yue Hu Xin-Hui Li Xin-Hui Li Xin-Hui Li Yu-Rong Qian Yu-Rong Qian Yu-Rong Qian Chao-Lin Song Chao-Lin Song Chao-Lin Song |
| author_sort | Jia-Ying Chen |
| collection | DOAJ |
| description | Protein function prediction is crucial in several key areas such as bioinformatics and drug design. With the rapid progress of deep learning technology, applying protein language models has become a research focus. These models utilize the increasing amount of large-scale protein sequence data to deeply mine its intrinsic semantic information, which can effectively improve the accuracy of protein function prediction. This review comprehensively combines the current status of applying the latest protein language models in protein function prediction. It provides an exhaustive performance comparison with traditional prediction methods. Through the in-depth analysis of experimental results, the significant advantages of protein language models in enhancing the accuracy and depth of protein function prediction tasks are fully demonstrated. |
| format | Article |
| id | doaj-art-cb29b24e492b4f648c21ccf1158e0647 |
| institution | Kabale University |
| issn | 2296-4185 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Bioengineering and Biotechnology |
| spelling | doaj-art-cb29b24e492b4f648c21ccf1158e06472025-01-21T08:37:03ZengFrontiers Media S.A.Frontiers in Bioengineering and Biotechnology2296-41852025-01-011310.3389/fbioe.2025.15065081506508Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive reviewJia-Ying Chen0Jia-Ying Chen1Jia-Ying Chen2Jing-Fu Wang3Jing-Fu Wang4Jing-Fu Wang5Yue Hu6Yue Hu7Yue Hu8Xin-Hui Li9Xin-Hui Li10Xin-Hui Li11Yu-Rong Qian12Yu-Rong Qian13Yu-Rong Qian14Chao-Lin Song15Chao-Lin Song16Chao-Lin Song17School of Software, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaSchool of Software, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaSchool of Software, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaSchool of Software, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaSchool of Computer Science and Technology, Xinjiang University, Urumqi, ChinaSchool of Software, Xinjiang University, Urumqi, ChinaKey Laboratory of Software Engineering, Xinjiang University, Urumqi, ChinaKey Laboratory of Signal Detection and Processing in Xinjiang Uygur Autonomous Region, Xinjiang University, Urumqi, ChinaProtein function prediction is crucial in several key areas such as bioinformatics and drug design. With the rapid progress of deep learning technology, applying protein language models has become a research focus. These models utilize the increasing amount of large-scale protein sequence data to deeply mine its intrinsic semantic information, which can effectively improve the accuracy of protein function prediction. This review comprehensively combines the current status of applying the latest protein language models in protein function prediction. It provides an exhaustive performance comparison with traditional prediction methods. Through the in-depth analysis of experimental results, the significant advantages of protein language models in enhancing the accuracy and depth of protein function prediction tasks are fully demonstrated.https://www.frontiersin.org/articles/10.3389/fbioe.2025.1506508/fullprotein function predictionprotein language modeldeep learningdeep multi-label classificationgene ontology (GO) |
| spellingShingle | Jia-Ying Chen Jia-Ying Chen Jia-Ying Chen Jing-Fu Wang Jing-Fu Wang Jing-Fu Wang Yue Hu Yue Hu Yue Hu Xin-Hui Li Xin-Hui Li Xin-Hui Li Yu-Rong Qian Yu-Rong Qian Yu-Rong Qian Chao-Lin Song Chao-Lin Song Chao-Lin Song Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review Frontiers in Bioengineering and Biotechnology protein function prediction protein language model deep learning deep multi-label classification gene ontology (GO) |
| title | Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review |
| title_full | Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review |
| title_fullStr | Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review |
| title_full_unstemmed | Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review |
| title_short | Evaluating the advancements in protein language models for encoding strategies in protein function prediction: a comprehensive review |
| title_sort | evaluating the advancements in protein language models for encoding strategies in protein function prediction a comprehensive review |
| topic | protein function prediction protein language model deep learning deep multi-label classification gene ontology (GO) |
| url | https://www.frontiersin.org/articles/10.3389/fbioe.2025.1506508/full |
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