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A robust, agnostic molecular biosignature based on machine learning.

ABSTRACT: The search for definitive biosignatures-unambiguous markers of past or present life-is a central goal of paleobiology and astrobiology. We used pyrolysis-gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine-learning methods, which resulted in a model that can identify the biogenicity of both contemporary and ancient geologically processed samples with ~90% accuracy. These machine-learning methods do not rely on precise compound identification: Rather, the relational aspects of chromatographic and mass peaks provide the needed information, which underscores this method's utility for detecting alien biology.

SUBMITTER: Cleaves HJ

PROVIDER: S-EPMC10576141 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature

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A robust, agnostic molecular biosignature based on machine learning.

Cleaves H James HJ Hystad Grethe G Prabhu Anirudh A Wong Michael L ML Cody George D GD Economon Sophia S Hazen Robert M RM

Proceedings of the National Academy of Sciences of the United States of America 20230925 41

The search for definitive biosignatures-unambiguous markers of past or present life-is a central goal of paleobiology and astrobiology. We used pyrolysis-gas chromatography coupled to mass spectrometry to analyze chemically disparate samples, including living cells, geologically processed fossil organic material, carbon-rich meteorites, and laboratory-synthesized organic compounds and mixtures. Data from each sample were employed as training and test subsets for machine-learning methods, which r ...[more]

PMID: 37748080

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A robust, agnostic molecular biosignature based on machine learning.

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A robust, agnostic molecular biosignature based on machine learning.

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