{"title":"学习对抗细胞刺激:博弈论视角。","authors":"Seyed Hamid Hosseini, Mahdi Imani","doi":"10.1109/cai54212.2023.00127","DOIUrl":null,"url":null,"abstract":"<p><p>Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.</p>","PeriodicalId":94276,"journal":{"name":"2023 IEEE Conference on Artificial Intelligence","volume":"2023 ","pages":"285-287"},"PeriodicalIF":0.0000,"publicationDate":"2023-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544835/pdf/nihms-1914207.pdf","citationCount":"0","resultStr":"{\"title\":\"Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective.\",\"authors\":\"Seyed Hamid Hosseini, Mahdi Imani\",\"doi\":\"10.1109/cai54212.2023.00127\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><p>Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.</p>\",\"PeriodicalId\":94276,\"journal\":{\"name\":\"2023 IEEE Conference on Artificial Intelligence\",\"volume\":\"2023 \",\"pages\":\"285-287\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10544835/pdf/nihms-1914207.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2023 IEEE Conference on Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/cai54212.2023.00127\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2023/8/2 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2023 IEEE Conference on Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/cai54212.2023.00127","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2023/8/2 0:00:00","PubModel":"Epub","JCR":"","JCRName":"","Score":null,"Total":0}
Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective.
Current genomics interventions have limitations in accounting for cell stimuli and the dynamic response to intervention. Although genomic sequencing and analysis have led to significant advances in personalized medicine, the complexity of cellular interactions and the dynamic nature of the cellular response to stimuli pose significant challenges. These limitations can lead to chronic disease recurrence and inefficient genomic interventions. Therefore, it is necessary to capture the full range of cellular responses to develop effective interventions. This paper presents a game-theoretic model of the fight between the cell and intervention, demonstrating analytically and numerically why current interventions become ineffective over time. The performance is analyzed using melanoma regulatory networks, and the role of artificial intelligence in deriving effective solutions is described.