From Sentences to Sequences: Rethinking Languages in Biological System
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866916823855792128 |
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| author | Liu, Ke Shen, Shuaike Chen, Hao |
| author_facet | Liu, Ke Shen, Shuaike Chen, Hao |
| contents | The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP successes can be effectively translated to biological domains. By treating the 3D structure of biomolecules as the semantic content of a sentence and accounting for the strong correlations between residues or bases, we highlight the importance of structural evaluation and demonstrate the applicability of the auto-regressive paradigm in biological language modeling. Code can be found at \href{https://github.com/zjuKeLiu/RiFold}{github.com/zjuKeLiu/RiFold} |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2507_00953 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | From Sentences to Sequences: Rethinking Languages in Biological System Liu, Ke Shen, Shuaike Chen, Hao Biomolecules Artificial Intelligence The paradigm of large language models in natural language processing (NLP) has also shown promise in modeling biological languages, including proteins, RNA, and DNA. Both the auto-regressive generation paradigm and evaluation metrics have been transferred from NLP to biological sequence modeling. However, the intrinsic structural correlations in natural and biological languages differ fundamentally. Therefore, we revisit the notion of language in biological systems to better understand how NLP successes can be effectively translated to biological domains. By treating the 3D structure of biomolecules as the semantic content of a sentence and accounting for the strong correlations between residues or bases, we highlight the importance of structural evaluation and demonstrate the applicability of the auto-regressive paradigm in biological language modeling. Code can be found at \href{https://github.com/zjuKeLiu/RiFold}{github.com/zjuKeLiu/RiFold} |
| title | From Sentences to Sequences: Rethinking Languages in Biological System |
| topic | Biomolecules Artificial Intelligence |
| url | https://arxiv.org/abs/2507.00953 |