学习对抗细胞刺激:博弈论视角。

Seyed Hamid Hosseini, Mahdi Imani
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引用次数: 0

摘要

目前的基因组学干预在解释细胞刺激和对干预的动态反应方面存在局限性。尽管基因组测序和分析在个性化医学方面取得了重大进展,但细胞相互作用的复杂性和细胞对刺激反应的动态性质带来了重大挑战。这些限制可能导致慢性病复发和低效的基因组干预。因此,有必要捕捉全方位的细胞反应,以制定有效的干预措施。本文提出了细胞和干预之间斗争的博弈论模型,从分析和数值上证明了当前干预措施随着时间的推移而无效的原因。使用黑色素瘤调控网络分析了其性能,并描述了人工智能在获得有效解决方案中的作用。
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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.

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Learning to Fight Against Cell Stimuli: A Game Theoretic Perspective. Structure-Based Inverse Reinforcement Learning for Quantification of Biological Knowledge.
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