Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformatics Article Swipe
Anqi Lin
,
Junxin Ye
,
Chang Qi
,
Lingxuan Zhu
,
Weiming Mou
,
Wenyi Gan
,
Dongqiang Zeng
,
Bufu Tang
,
Mingjia Xiao
,
Guangdi Chu
,
Shengkun Peng
,
Hank Z. H. Wong
,
Lin Zhang
,
Hengguo Zhang
,
Xinpei Deng
,
Kailai Li
,
Jian Zhang
,
Aimin Jiang
,
Zhengrui Li
,
Peng Luo
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf357
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1093/bib/bbaf357
Large language models (LLMs), representing a breakthrough advancement in artificial intelligence, have demonstrated substantial application value and development potential in bioinformatics research, particularly showing significant progress in the processing and analysis of complex biological data. This comprehensive review systematically examines the development and applications of LLMs in bioinformatics, with particular emphasis on their advancements in protein and nucleic acid structure prediction, omics analysis, drug design and screening, and biomedical literature mining. This work highlights the distinctive capabilities of LLMs in end-to-end learning and knowledge transfer paradigms. Additionally, this paper thoroughly discusses the major challenges confronting LLMs in current applications, including key issues such as model interpretability and data bias. Furthermore, this review comprehensively explores the potential of LLMs in cross-modal learning and interdisciplinary development. In conclusion, this paper aims to systematically summarize the current research status of LLMs in bioinformatics, objectively evaluate their advantages and limitations, and provide insights and recommendations for future research directions, thereby positioning LLMs as essential tools in bioinformatics research and fostering innovative developments in the biomedical field.
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- Type
- review
- Language
- en
- Landing Page
- https://doi.org/10.1093/bib/bbaf357
- OA Status
- gold
- Cited By
- 3
- References
- 118
- Related Works
- 10
- OpenAlex ID
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All OpenAlex metadata
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https://openalex.org/W4412654797Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1093/bib/bbaf357Digital Object Identifier
- Title
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Bridging artificial intelligence and biological sciences: a comprehensive review of large language models in bioinformaticsWork title
- Type
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reviewOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-07-01Full publication date if available
- Authors
-
Anqi Lin, Junxin Ye, Chang Qi, Lingxuan Zhu, Weiming Mou, Wenyi Gan, Dongqiang Zeng, Bufu Tang, Mingjia Xiao, Guangdi Chu, Shengkun Peng, Hank Z. H. Wong, Lin Zhang, Hengguo Zhang, Xinpei Deng, Kailai Li, Jian Zhang, Aimin Jiang, Zhengrui Li, Peng LuoList of authors in order
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https://doi.org/10.1093/bib/bbaf357Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.1093/bib/bbaf357Direct OA link when available
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Bridging (networking), Computer science, Data science, Artificial intelligence, Computer networkTop concepts (fields/topics) attached by OpenAlex
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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118Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.(LLMs), | 4 |
| abstract_inverted_index.complex | 33 |
| abstract_inverted_index.current | 98, 134 |
| abstract_inverted_index.mining. | 71 |
| abstract_inverted_index.nucleic | 58 |
| abstract_inverted_index.protein | 56 |
| abstract_inverted_index.provide | 148 |
| abstract_inverted_index.showing | 24 |
| abstract_inverted_index.thereby | 156 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.analysis | 31 |
| abstract_inverted_index.emphasis | 51 |
| abstract_inverted_index.evaluate | 142 |
| abstract_inverted_index.examines | 40 |
| abstract_inverted_index.explores | 114 |
| abstract_inverted_index.insights | 149 |
| abstract_inverted_index.language | 2 |
| abstract_inverted_index.learning | 82, 121 |
| abstract_inverted_index.progress | 26 |
| abstract_inverted_index.research | 135, 154, 164 |
| abstract_inverted_index.transfer | 85 |
| abstract_inverted_index.analysis, | 63 |
| abstract_inverted_index.discusses | 91 |
| abstract_inverted_index.essential | 160 |
| abstract_inverted_index.fostering | 166 |
| abstract_inverted_index.including | 100 |
| abstract_inverted_index.knowledge | 84 |
| abstract_inverted_index.potential | 19, 116 |
| abstract_inverted_index.research, | 22 |
| abstract_inverted_index.structure | 60 |
| abstract_inverted_index.summarize | 132 |
| abstract_inverted_index.advantages | 144 |
| abstract_inverted_index.artificial | 10 |
| abstract_inverted_index.biological | 34 |
| abstract_inverted_index.biomedical | 69, 171 |
| abstract_inverted_index.challenges | 94 |
| abstract_inverted_index.end-to-end | 81 |
| abstract_inverted_index.highlights | 74 |
| abstract_inverted_index.innovative | 167 |
| abstract_inverted_index.literature | 70 |
| abstract_inverted_index.paradigms. | 86 |
| abstract_inverted_index.particular | 50 |
| abstract_inverted_index.processing | 29 |
| abstract_inverted_index.screening, | 67 |
| abstract_inverted_index.thoroughly | 90 |
| abstract_inverted_index.advancement | 8 |
| abstract_inverted_index.application | 15 |
| abstract_inverted_index.conclusion, | 126 |
| abstract_inverted_index.confronting | 95 |
| abstract_inverted_index.cross-modal | 120 |
| abstract_inverted_index.development | 18, 42 |
| abstract_inverted_index.directions, | 155 |
| abstract_inverted_index.distinctive | 76 |
| abstract_inverted_index.objectively | 141 |
| abstract_inverted_index.positioning | 157 |
| abstract_inverted_index.prediction, | 61 |
| abstract_inverted_index.significant | 25 |
| abstract_inverted_index.substantial | 14 |
| abstract_inverted_index.Furthermore, | 110 |
| abstract_inverted_index.advancements | 54 |
| abstract_inverted_index.applications | 44 |
| abstract_inverted_index.breakthrough | 7 |
| abstract_inverted_index.capabilities | 77 |
| abstract_inverted_index.demonstrated | 13 |
| abstract_inverted_index.development. | 124 |
| abstract_inverted_index.developments | 168 |
| abstract_inverted_index.limitations, | 146 |
| abstract_inverted_index.particularly | 23 |
| abstract_inverted_index.representing | 5 |
| abstract_inverted_index.Additionally, | 87 |
| abstract_inverted_index.applications, | 99 |
| abstract_inverted_index.comprehensive | 37 |
| abstract_inverted_index.intelligence, | 11 |
| abstract_inverted_index.bioinformatics | 21, 163 |
| abstract_inverted_index.systematically | 39, 131 |
| abstract_inverted_index.bioinformatics, | 48, 140 |
| abstract_inverted_index.comprehensively | 113 |
| abstract_inverted_index.recommendations | 151 |
| abstract_inverted_index.interpretability | 106 |
| abstract_inverted_index.interdisciplinary | 123 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 96 |
| countries_distinct_count | 5 |
| institutions_distinct_count | 20 |
| citation_normalized_percentile.value | 0.9434644 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |