Evidence map›Paper›PMID 36909508›Full record

ArticleResearch square2023

Stabl: sparse and reliable biomarker discovery in predictive modeling of high-dimensional omic data.

Julien Hédou, Ivana Marić, Grégoire Bellan, Jakob Einhaus, Dyani K Gaudillière, Francois-Xavier Ladant, Franck Verdonk, Ina A Stelzer, Dorien Feyaerts, Amy S Tsai and 11 more

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In one paragraph

Article in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing 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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

21 authors.

Julien HédouDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Ivana MarićDepartment of Pediatrics, Stanford University, Stanford, CA.ORCID 0000-0002-9441-521X
Grégoire BellanTélécom Paris, Paris, France.ORCID 0000-0002-8804-8062
Jakob EinhausDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Dyani K GaudillièreDivision of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA.ORCID 0000-0003-3017-5939
Francois-Xavier LadantDepartment of Economics, Harvard University, Cambridge, MA.
Franck VerdonkDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Ina A StelzerDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.ORCID 0000-0002-9974-4661
Dorien FeyaertsDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Amy S TsaiDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.ORCID 0000-0002-2380-6424
Edward A GanioDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Maximilian SabayevDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Joshua GillardDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Thomas A BonhamDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Masaki SatoDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Maïgane DiopDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.
Martin S AngstDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.ORCID 0000-0002-1550-8136
David StevensonDepartment of Pediatrics, Stanford University, Stanford, CA.
Nima AghaeepourDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.ORCID 0000-0002-6117-8764
Andrea MontanariDepartment of Statistics, Stanford University, Stanford, CA.
Brice GaudillièreDepartment of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA.ORCID 0000-0002-3475-5706

Funding

UCSF Stanford Endometriosis Center for Discovery, Innovation, Training and Community EngagementP01HD106414 · NICHD · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI OPOKU-ANANE, JESSICA · 2021 to 2025
$7.1M
Harnessing the human monocyte system to improve surgical recoveryR35GM137936 · NIGMS · STANFORD UNIVERSITY · PI GAUDILLIERE, BRICE · 2020 to 2024
$2.0M
Neuromodulation of maternal immune adaptations in pregnancyK99HD105016 · NICHD · STANFORD UNIVERSITY · PI STELZER, INA · 2021 to 2022
$263k
NICHD NIH HHS K99 HD105016NICHD NIH HHS P01 HD106414NIGMS NIH HHS R35 GM137936
6 · The paper itself

Abstract

High-content omic technologies coupled with sparsity-promoting regularization methods (SRM) have transformed the biomarker discovery process. However, the translation of computational results into a clinical use-case scenario remains challenging. A rate-limiting step is the rigorous selection of reliable biomarker candidates among a host of biological features included in multivariate models. We propose Stabl, a machine learning framework that unifies the biomarker discovery process with multivariate predictive modeling of clinical outcomes by selecting a sparse and reliable set of biomarkers. Evaluation of Stabl on synthetic datasets and four independent clinical studies demonstrates improved biomarker sparsity and reliability compared to commonly used SRMs at similar predictive performance. Stabl readily extends to double- and triple-omics integration tasks and identifies a sparser and more reliable set of biomarkers than those selected by state-of-the-art early- and late-fusion SRMs, thereby facilitating the biological interpretation and clinical translation of complex multi-omic predictive models. The complete package for Stabl is available online at https://github.com/gregbellan/Stabl.

Identifiers

PMID36909508
PMCPMC10002850

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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.