进化会产生什么? 在活生生的血统和机器中学习
Benedikt Hartl1, Michael Levin2
1Allen Discovery Center at Tufts University, Medford, MA, USA; Institute for Theoretical Physics, TU Wien, Vienna, Austria.
Trends in genetics : TIG
|June 11, 2025
概括
基因组信息通过发育遗传学和机器学习创造了自我构建的生物. 进化和学习共享对称性,基因组作为解决问题的生成模型.
科学领域:
- 发展遗传学 发展遗传学
- 机器学习 机器学习
- 进化生物学 进化生物学
- 神经科学是一个神经科学.
背景情况:
- 了解基因组信息如何导致复杂,解决问题的生物体是一个基本的生物学问题.
- 基因和特征之间的关系是复杂的,涉及生理计算和可塑性.
- 传统的方法往往将基因型-表型地图视为复杂和间接的.
研究的目的:
- 审查最近的进展,统一发育遗传学和机器学习 (ML) 用于基因特征映射.
- 突出进化与学习之间的相似之处,将基因组作为一个生成模型.
- 探索ML和神经科学如何为理解进化学习和发育过程提供定量形式.
主要方法:
- 审查目前的研究整合发育遗传学和ML.
- 强调基因组概念作为一个生成模型.
- 应用ML和神经科学原则来理解生物信息处理.
主要成果:
- 确定了进化过程和机器学习之间的深度对称性.
- 他将生理计算描述为可塑性和强度的来源,而不仅仅是复杂性的来源.
- 证明了ML和神经科学形式主义对解释进化学习和形态发生的有用性.
结论:
- 基因组可以被理解为一种生成模型,是由进化学习塑造的.
- 生理计算是有机体强度和解决问题的关键.
- 这种跨学科的方法为遗传学,进化发育生物学,再生医学和合成形态工程提供了新的见解.
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