Samuel A. Donkor, Matthew E. Walsh, Alexander J. Titus
{"title":"Computing in the Life Sciences: From Early Algorithms to Modern AI","authors":"Samuel A. Donkor, Matthew E. Walsh, Alexander J. Titus","doi":"arxiv-2406.12108","DOIUrl":null,"url":null,"abstract":"Computing in the life sciences has undergone a transformative evolution, from\nearly computational models in the 1950s to the applications of artificial\nintelligence (AI) and machine learning (ML) seen today. This paper highlights\nkey milestones and technological advancements through the historical\ndevelopment of computing in the life sciences. The discussion includes the\ninception of computational models for biological processes, the advent of\nbioinformatics tools, and the integration of AI/ML in modern life sciences\nresearch. Attention is given to AI-enabled tools used in the life sciences,\nsuch as scientific large language models and bio-AI tools, examining their\ncapabilities, limitations, and impact to biological risk. This paper seeks to\nclarify and establish essential terminology and concepts to ensure informed\ndecision-making and effective communication across disciplines.","PeriodicalId":501219,"journal":{"name":"arXiv - QuanBio - Other Quantitative Biology","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuanBio - Other Quantitative Biology","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2406.12108","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
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.