A Conceptual Framework for Human-AI Collaborative Genome Annotation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866908291505848320 |
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| author | Li, Xiaomei Whan, Alex McNeil, Meredith Starns, David Irons, Jessica Andrew, Samuel C. Suchecki, Rad |
| author_facet | Li, Xiaomei Whan, Alex McNeil, Meredith Starns, David Irons, Jessica Andrew, Samuel C. Suchecki, Rad |
| contents | Genome annotation is essential for understanding the functional elements within genomes. While automated methods are indispensable for processing large-scale genomic data, they often face challenges in accurately predicting gene structures and functions. Consequently, manual curation by domain experts remains crucial for validating and refining these predictions. These combined outcomes from automated tools and manual curation highlight the importance of integrating human expertise with AI capabilities to improve both the accuracy and efficiency of genome annotation. However, the manual curation process is inherently labor-intensive and time-consuming, making it difficult to scale for large datasets. To address these challenges, we propose a conceptual framework, Human-AI Collaborative Genome Annotation (HAICoGA), which leverages the synergistic partnership between humans and artificial intelligence to enhance human capabilities and accelerate the genome annotation process. Additionally, we explore the potential of integrating Large Language Models (LLMs) into this framework to support and augment specific tasks. Finally, we discuss emerging challenges and outline open research questions to guide further exploration in this area. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_23691 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Conceptual Framework for Human-AI Collaborative Genome Annotation Li, Xiaomei Whan, Alex McNeil, Meredith Starns, David Irons, Jessica Andrew, Samuel C. Suchecki, Rad Genomics Human-Computer Interaction Genome annotation is essential for understanding the functional elements within genomes. While automated methods are indispensable for processing large-scale genomic data, they often face challenges in accurately predicting gene structures and functions. Consequently, manual curation by domain experts remains crucial for validating and refining these predictions. These combined outcomes from automated tools and manual curation highlight the importance of integrating human expertise with AI capabilities to improve both the accuracy and efficiency of genome annotation. However, the manual curation process is inherently labor-intensive and time-consuming, making it difficult to scale for large datasets. To address these challenges, we propose a conceptual framework, Human-AI Collaborative Genome Annotation (HAICoGA), which leverages the synergistic partnership between humans and artificial intelligence to enhance human capabilities and accelerate the genome annotation process. Additionally, we explore the potential of integrating Large Language Models (LLMs) into this framework to support and augment specific tasks. Finally, we discuss emerging challenges and outline open research questions to guide further exploration in this area. |
| title | A Conceptual Framework for Human-AI Collaborative Genome Annotation |
| topic | Genomics Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.23691 |