  The search for atmospheric biosignatures in Earth-like exoplanets is one of
the most pressing challenges in observational astrobiology. Detecting biogenic
gases in terrestrial planets requires high resolution and long integration
times. In this work, we developed and tested a machine-learning general
methodology, intended to classify transmission spectra with low Signal-to-Noise
Ratio according to their potential to contain biosignatures. For that purpose,
we trained a set of models capable of classifying noisy transmission spectra,
as having methane, ozone, and/or water (multilabel classification), or simply
as being interesting for follow-up observations (binary classification). The
models were trained with $\sim\, 10^6$ synthetic spectra of planets similar to
TRAPPIST-1 e, which were generated with the package MultiREx, especially
developed for this work. The trained algorithms correctly classified test
planets with transmission spectra having SNR $<$ 6 and containing methane
and/or ozone at mixing ratios similar to those of modern and Proterozoic Earth.
Tests on realistic synthetic spectra based on the current Earth\'s atmosphere
show at least one of our models would classify as likely having biosignatures
and using only one transit, most of the inhabited terrestrial planets observed
with the JWST/NIRSpec PRISM around M-dwarfs located at distances similar or
smaller than that of TRAPPIST-1 e. The implication of this result for the
designing of observing programs and future surveys is enormous since
machine-assisted strategies similar to those presented here could significantly
optimize the usage of JWST resources for biosignature searching, while
maximizing the chances of a real discovery after dedicated follow-up
observations of promising candidates.
