Discovery of sparse, reliable omic biomarkers with Stabl

Adoption of high-content omic technologies in clinical studies, coupled
with computational methods, have yielded an abundance of candidate
biomarkers. However, translating such findings into bona fide clinical
biomarkers remains challenging. To facilitate this process, we introduce
Stabl, a general machine learning framework that identifies a sparse,
reliable set of biomarkers by integrating noise injection and a
data-driven signal-to-noise threshold into multivariable predictive
modeling. Evaluation of Stabl on synthetic datasets and five independent
clinical studies demonstrates improved biomarker sparsity and reliability
compared to commonly used sparsity-promoting regularization methods while
maintaining predictive performance; it distills datasets containing 1,400
to 35,000 features down to 4 to 34 candidate biomarkers. Stabl extends to
multi-omic integration tasks, enabling biological interpretation of
complex predictive models, as it hones in on a shortlist of proteomic,
metabolomic, and cytometric events predicting labor onset, microbial
biomarkers of preterm birth, and a pre-operative immune signature of
post-surgical infections.