Bridge2AI Recommendations for AI-Ready Genomic Data
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arXiv
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| Autores principales: | , , , , , , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866909957853544448 |
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| author | Cannon, Matthew Goar, Wesley Lee, In-Hee Stevenson, James Heiser, Amy Sheffield, Nathan Eddy, James Munoz-Torres, Monica Kong, Sek Wong Wagner, Alex H |
| author_facet | Cannon, Matthew Goar, Wesley Lee, In-Hee Stevenson, James Heiser, Amy Sheffield, Nathan Eddy, James Munoz-Torres, Monica Kong, Sek Wong Wagner, Alex H |
| contents | Rapid advancements in technology have led to an increased use of artificial intelligence (AI) technologies in medicine and bioinformatics research. In anticipation of this, the National Institutes of Health (NIH) assembled the Bridge to Artificial Intelligence (Bridge2AI) consortium to coordinate development of AI-ready datasets that can be leveraged by AI models to address grand challenges in human health and disease. The widespread availability of genome sequencing technologies for biomedical research presents a key data type for informing AI models, necessitating that genomics data sets are AI-ready. To this end, the Genomic Information Standards Team (GIST) of the Bridge2AI Standards Working Group has documented a set of recommendations for maintaining AI-ready genomics datasets. In this report, we describe recommendations for the collection, storage, identification, and proper use of genomics datasets to enable them to be considered AI-ready and thus drive new insights in medicine through AI and machine learning applications. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_11519 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Bridge2AI Recommendations for AI-Ready Genomic Data Cannon, Matthew Goar, Wesley Lee, In-Hee Stevenson, James Heiser, Amy Sheffield, Nathan Eddy, James Munoz-Torres, Monica Kong, Sek Wong Wagner, Alex H Other Quantitative Biology Rapid advancements in technology have led to an increased use of artificial intelligence (AI) technologies in medicine and bioinformatics research. In anticipation of this, the National Institutes of Health (NIH) assembled the Bridge to Artificial Intelligence (Bridge2AI) consortium to coordinate development of AI-ready datasets that can be leveraged by AI models to address grand challenges in human health and disease. The widespread availability of genome sequencing technologies for biomedical research presents a key data type for informing AI models, necessitating that genomics data sets are AI-ready. To this end, the Genomic Information Standards Team (GIST) of the Bridge2AI Standards Working Group has documented a set of recommendations for maintaining AI-ready genomics datasets. In this report, we describe recommendations for the collection, storage, identification, and proper use of genomics datasets to enable them to be considered AI-ready and thus drive new insights in medicine through AI and machine learning applications. |
| title | Bridge2AI Recommendations for AI-Ready Genomic Data |
| topic | Other Quantitative Biology |
| url | https://arxiv.org/abs/2512.11519 |