Computing in the Life Sciences: From Early Algorithms to Modern AI
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866917699332866048 |
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| author | Donkor, Samuel A. Walsh, Matthew E. Titus, Alexander J. |
| author_facet | Donkor, Samuel A. Walsh, Matthew E. Titus, Alexander J. |
| contents | Computing in the life sciences has undergone a transformative evolution, from early computational models in the 1950s to the applications of artificial intelligence (AI) and machine learning (ML) seen today. This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk. This paper seeks to clarify and establish essential terminology and concepts to ensure informed decision-making and effective communication across disciplines. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_12108 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Computing in the Life Sciences: From Early Algorithms to Modern AI Donkor, Samuel A. Walsh, Matthew E. Titus, Alexander J. Other Quantitative Biology Artificial Intelligence Computing in the life sciences has undergone a transformative evolution, from early computational models in the 1950s to the applications of artificial intelligence (AI) and machine learning (ML) seen today. This paper highlights key milestones and technological advancements through the historical development of computing in the life sciences. The discussion includes the inception of computational models for biological processes, the advent of bioinformatics tools, and the integration of AI/ML in modern life sciences research. Attention is given to AI-enabled tools used in the life sciences, such as scientific large language models and bio-AI tools, examining their capabilities, limitations, and impact to biological risk. This paper seeks to clarify and establish essential terminology and concepts to ensure informed decision-making and effective communication across disciplines. |
| title | Computing in the Life Sciences: From Early Algorithms to Modern AI |
| topic | Other Quantitative Biology Artificial Intelligence |
| url | https://arxiv.org/abs/2406.12108 |