Discriminating abiotic and biotic organics in meteorite and terrestrial samples using machine learning on mass spectrometry data.

Daniel Saeedi, Denise Buckner, Thomas A Walton, José C Aponte, Amirali Aghazadeh

Abstract read

In one paragraph

Article in PNAS nexus, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it

0cells of the map it votes in

1citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

An organics-forward approach to searching for life on Mars.Nature communications · 2026
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Daniel SaeediSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID https://orcid.org/0009-0000-0229-4776

Denise BucknerAstrochemistry Laboratory, Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA.ORCID https://orcid.org/0000-0002-5160-5857

Thomas A WaltonSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.

José C AponteAstrochemistry Laboratory, Goddard Space Flight Center, NASA, Greenbelt, MD 20771, USA.ORCID https://orcid.org/0000-0002-0131-1981

Amirali AghazadehSchool of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332, USA.ORCID https://orcid.org/0000-0003-0223-0873

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the upcoming sample return missions to the Solar System where traces of past, extinct, or present life may be found, there is an urgent need to develop unbiased methods that can distinguish molecular distributions of organic compounds synthesized abiotically from those produced biotically but were subsequently altered through diagenetic processes. We conducted untargeted analyses on a collection of meteorite and terrestrial geologic samples using 2D gas chromatography coupled with high-resolution time-of-flight mass spectrometry and compared their soluble nonpolar and semipolar organic species. To deconvolute the resulting large dataset, we developed LifeTracer, a computational framework for processing and downstream machine learning analysis of mass spectrometry data. LifeTracer identified predictive molecular features that distinguish abiotic from biotic origins and enabled a robust classification of meteorites from terrestrial samples based on the composition of their nonpolar soluble organics.

Indexed as

astrobiologymachine learningmass spectrometrymeteorites

Identifiers

PMID41262978

DOI10.1093/pnasnexus/pgaf334

PMCPMC12624505

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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.