ArticleProceedings of the National Academy of Sciences of the United States of America2023
A robust, agnostic molecular biosignature based on machine learning.
Article in Proceedings of the National Academy of Sciences of the United States of America, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- From searching for extraterrestrial biosignatures to exploring the origins of life: A prebiotic chemistry perspective.Fundamental research · 2026Review
- Molecular assembly as a universal biosignature measurable by mass spectrometry.Proceedings of the National Academy of Sciences of the United States of America · 2026Article
- Proto-Biosignatures and Planetary Geochemical Metabolism: A Thermodynamic Screening Model of Prebiotic Geochemical Organization.Life (Basel, Switzerland) · 2026Article
- Mineral biosignature identification from Raman spectroscopy using machine learning.PNAS nexus · 2026Article
- Agnostic material classification using differential de Bruijn graphs of DNA imprints.bioRxiv : the preprint server for biology · 2026Article
- Organic geochemical evidence for life in Archean rocks identified by pyrolysis-GC-MS and supervised machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2025Article
- Origin of life: β-sheet amyloid conformers as the primordial functional polymers on the early Earth and their role in the emergence of complex dynamic networks.FEBS letters · 2025Review
- Fossilized dinosaur cells that defied the ravages of time - 20 years since a key discovery.Nature · 2025Article
- Fundamental constraints to the logic of living systems.Interface focus · 2024Review
- Artificial intelligence for geoscience: Progress, challenges, and perspectives.Innovation (Cambridge (Mass.)) · 2024Review
- Alternative Pathways in Astrobiology: Reviewing and Synthesizing Contingency and Non-Biomolecular Origins of Terrestrial and Extraterrestrial Life.Life (Basel, Switzerland) · 2024Review
- A robust, agnostic molecular biosignature based on machine learning.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.