通过热解-GC-MS和监督机器学习识别的古岩石中生命的有机地化学证据
Michael L Wong1,2, Anirudh Prabhu1, Conel O'D Alexander1
1Earth and Planets Laboratory, Carnegie Institution for Science, Washington, DC 20015.
概括
岩石中的古老有机分子可以揭示生命的历史. 机器学习成功地确定了化石生物的起源和特征,甚至来自数十亿年前.
科学领域:
- 地质化学 地质化学
- 古生物学的古生物学
- 天体生物学 天体生物学
背景情况:
- 有机分子被保存在沉积岩中,来自生命和非生命来源.
- 地质过程在很长一段时间内改变了这些分子,使得它们的分析具有挑战性.
- 古代分子化石有可能揭示诊断生物分子信息.
研究的目的:
- 开发和验证机器学习模型,以区分古代有机分子的起源和特征.
- 研究古代化石中分子生物标志的生存和诊断潜力.
- 为了确定早期生命和新陈代谢过程的证据,在Paleoarchean和Neoarchean岩石中.
主要方法:
- 分析406个不同的样本 (化石,现代生物,石,合成) 使用热解气体染色学和质谱学.
- 监督机器学习 (随机森林模型) 对分析数据的应用.
- 根据生物原与非生物原的起源,遗传学亲缘关系 (植物与动物) 和生理学 (光合作用与非光合作用) 进行样本的分类.
主要成果:
- 随机森林模型在对对分类中取得了高准确性:现代与化石/石有机物 (100%),化石植物与石有机物 (97%),现代与化石植物 (98%),现代植物与动物 (95%).
- 将化石生物原样与非生物原样区分开来,准确度达到了93%.
- 分类光合作用与非光合作用样本 (现代和古代) 也导致93%的准确性.
- 提出了在3.33亿年前的古老岩石中生物分子组合和在2.52亿年前的新古岩石中光自的证据.
结论:
- 分子生物标志可以在古代化石中存活下来,使生物起源和特征的识别成为可能.
- 机器学习是一种强大的工具,可以从古老样本中解读复杂的分子数据.
- 这项研究提供了支持地球历史早期生命和代谢多样性的分子证据.
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