Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification
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arXiv
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| Auteurs principaux: | , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866911293058842624 |
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| author | Liu, Zicheng Li, Siyuan Chen, Zhiyuan Yu, Chang Yang, Qirong Guo, Yucheng Yang, Yujie Zhang, Xiaoming Li, Stan Z. |
| author_facet | Liu, Zicheng Li, Siyuan Chen, Zhiyuan Yu, Chang Yang, Qirong Guo, Yucheng Yang, Yujie Zhang, Xiaoming Li, Stan Z. |
| contents | The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. Although modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains underexplored. This paper follows the guidance of the central dogma to redesign both the data and model pipeline and offers a comprehensive framework, Life-Code, that spans different biological functions. As for data flow, we propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences. As for the model, we design a codon tokenizer and a hybrid long-sequence architecture to encode the interactions between coding and non-coding regions through masked modeling pre-training. To model the translation and folding process with coding sequences, Life-Code learns protein structures of the corresponding amino acids by knowledge distillation from off-the-shelf protein language models. Such designs enable Life-Code to capture complex interactions within genetic sequences, providing a more comprehensive understanding of multi-omics with the central dogma. Extensive experiments show that Life-Code achieves state-of-the-art results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2502_07299 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification Liu, Zicheng Li, Siyuan Chen, Zhiyuan Yu, Chang Yang, Qirong Guo, Yucheng Yang, Yujie Zhang, Xiaoming Li, Stan Z. Machine Learning Artificial Intelligence Computation and Language Genomics The interactions between DNA, RNA, and proteins are fundamental to biological processes, as illustrated by the central dogma of molecular biology. Although modern biological pre-trained models have achieved great success in analyzing these macromolecules individually, their interconnected nature remains underexplored. This paper follows the guidance of the central dogma to redesign both the data and model pipeline and offers a comprehensive framework, Life-Code, that spans different biological functions. As for data flow, we propose a unified pipeline to integrate multi-omics data by reverse-transcribing RNA and reverse-translating amino acids into nucleotide-based sequences. As for the model, we design a codon tokenizer and a hybrid long-sequence architecture to encode the interactions between coding and non-coding regions through masked modeling pre-training. To model the translation and folding process with coding sequences, Life-Code learns protein structures of the corresponding amino acids by knowledge distillation from off-the-shelf protein language models. Such designs enable Life-Code to capture complex interactions within genetic sequences, providing a more comprehensive understanding of multi-omics with the central dogma. Extensive experiments show that Life-Code achieves state-of-the-art results on various tasks across three omics, highlighting its potential for advancing multi-omics analysis and interpretation. |
| title | Life-Code: Central Dogma Modeling with Multi-Omics Sequence Unification |
| topic | Machine Learning Artificial Intelligence Computation and Language Genomics |
| url | https://arxiv.org/abs/2502.07299 |