---
title: "Sparse ML Pipeline with Stabl Reveals Cord Blood Multi-Omic Signatures Linked to Bronchopulmonary"
id: "biorxiv-8-sparse-machine-learning-pipeline-with-stabl-identifies-cord-blood-multi-omic"
canonical_url: "https://medichelpline.com/clinical-feed/biorxiv-8-sparse-machine-learning-pipeline-with-stabl-identifies-cord-blood-multi-omic"
content_type: "clinical_feed_article"
specialty: "Pediatrics"
source_name: "bioRxiv (Biomedical Preprints)"
source_url: "https://www.biorxiv.org/content/10.64898/2026.09.12.748996v1?rss=1"
published_at: "2026-09-18T12:00:00.000Z"
evidence_level: "Verified Feed"
license: "CC-BY-NC-4.0 / Informational Use"
---
# Sparse ML Pipeline with Stabl Reveals Cord Blood Multi-Omic Signatures Linked to Bronchopulmonary
## Provenance & Clinical Metadata
- **Canonical URL:** https://medichelpline.com/clinical-feed/biorxiv-8-sparse-machine-learning-pipeline-with-stabl-identifies-cord-blood-multi-omic
- **Specialty:** [Pediatrics](https://medichelpline.com/clinical-feed/pediatrics.md)
- **Primary Source:** bioRxiv (Biomedical Preprints)
- **Source URL:** [Original Journal Publication](https://www.biorxiv.org/content/10.64898/2026.09.12.748996v1?rss=1)
- **Published At:** 2026-09-18T12:00:00.000Z
- **Evidence Rating:** Verified Feed
## Executive GIST (TL;DR)
- This study used a sparse machine learning pipeline combining **Stabl** and LASSO to integrate cord blood **metabolomics**, **proteomics**, and **adductomics** data to identify biomarkers associated with bronchopulmonary dysplasia (BPD). - The analysis used a well-characterized birth cohort of 217 infants: 52 term and 165 extremely preterm (<28 weeks). Among preterm infants, 82 had BPD and 35 had severe BPD or death. - A total of approximately 45,000 molecular features were measured and entered into sparse multivariable models. - Both LASSO and Stabl produced a perfect predictive signature for preterm birth (AUROC = 1.0; p < 0.001). - In analyses restricted to the preterm group, models showed strong predictive performance for severe BPD (AUROC = 0.83; p = 0.005). - **Stabl** selected a 12-biomarker signature (2 adducts, 3 proteins, 7 metabolites) that predicted grade III BPD with good discrimination (AUROC = 0.76; p = 0.03). - Identified biomarkers implicated dysregulation in innate and adaptive immune responses, metabolic programming, and oxidative stress pathways. - The authors conclude that a sparse ML pipeline is a complementary approach for discovering novel pathways and biomarkers underlying multifactorial BPD and its endotypes. - Funding sources reported include NIH grants R01HL139798 and R21HD100831. - The authors declared no competing interests. - Supplementary material and data/code links were referenced by the preprint but detailed limitations or external validation were not reported in the source abstract.
## Clinical Analysis & Structured Key Points
Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia | bioRxiv Skip to main content New Results Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia View ORCID Profile Karen Mestan , Janu Newar , Jiaqi Zhao , Abhik Chakraborty , Jonathan Reiss , William Funk , Ina Stelzer , Benjamin Waked , Gregory Bellan , Xavier Durand , Julian Hedou doi: https://doi.org/10.64898/2026.09.12.748996 Karen Mestan 1 University of California San Diego; Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Karen Mestan For correspondence: kmestan{at}health.ucsd.edu Janu Newar 1 University of California San Diego; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jiaqi Zhao 1 University of California San Diego; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abhik Chakraborty 1 University of California San Diego; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Jonathan Reiss 2 Stanford University; Find this author on Google Scholar Find this author on PubMed Search for this author on this site William Funk 3 Northwestern University; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ina Stelzer 1 University of California San Diego; Find this author on Google Scholar Find this author on PubMed Search for this author on this site Benjamin Waked 4 SurgeCare Find this author on Google Scholar Find this author on PubMed Search for this author on this site Gregory Bellan 4 SurgeCare Find this author on Google Scholar Find this author on PubMed Search for this author on this site Xavier Durand 4 SurgeCare Find this author on Google Scholar Find this author on PubMed Search for this author on this site Julian Hedou 4 SurgeCare Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background: Several omics studies have been completed in recent years, with the goal of identifying biomarkers of complex multifactorial diseases, such as bronchopulmonary dysplasia (BPD). Objective: To evaluate the performance of 3 distinct omics platforms, using a machine learning pipeline with integration of sparse, reliable and adaptive biomarker identification (Stabl). Methods: Using a well-characterized birth cohort, cord blood metabolomics, proteomics and adductomics data were integrated with Least Absolute Shrinkage and Selection Operator (LASSO) regression and Stabl, to evaluate predictive performance for BPD. Results: Sparse multivariable modeling of 45,000 features measured in 217 infants (52 term, 165 extremely preterm <28 weeks; 82 with BPD and 35 with severe BPD/death) identified a perfect signature for preterm birth with both LASSO and Stabl (AUROC=1.0; p<0.001). Analysis of the preterm group yielded excellent predictive power for severe BPD (AUROC=0.83; p=0.005). Stabl identified a set of 12 biomarkers (2 adducts, 3 proteins and 7 metabolites) with good performance for predicting grade III BPD (AUROC=0.76; P=0.03). Biomarkers across the 3 omics platforms revealed dysregulated pathways of innate/adaptive immune responses, metabolic programming and oxidative stress. Conclusions: The sparse machine learning pipeline is a complementary approach for identifying novel pathways and biomarkers of multifactorial BPD and its endotypes. Competing Interest Statement The authors have declared no competing interest. Footnotes https://doi.org/10.6084/m9.figshare.33147644 Funder Information Declared NIH , R01HL139798 , R21HD100831 Copyright The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. All rights reserved. No reuse allowed without permission. Back to top Previous Next Posted September 18, 2026. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about bioRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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Share Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia Karen Mestan , Janu Newar , Jiaqi Zhao , Abhik Chakraborty , Jonathan Reiss , William Funk , Ina Stelzer , Benjamin Waked , Gregory Bellan , Xavier Durand , Julian Hedou bioRxiv 2026.09.12.748996; doi: https://doi.org/10.64898/2026.09.12.748996 Share This Article: Copy Citation Tools Sparse Machine Learning Pipeline with Stabl Identifies Cord Blood Multi-Omic Signatures of Bronchopulmonary Dysplasia Karen Mestan , Janu Newar , Jiaqi Zhao , Abhik Chakraborty , Jonathan Reiss , William Funk , Ina Stelzer , Benjamin Waked , Gregory Bellan , Xavier Durand , Julian Hedou bioRxiv 2026.09.12.748996; doi: https://doi.org/10.64898/2026.09.12.748996 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Areas All Articles Animal Behavior and Cognition (8010) Biochemistry (18733) Bioengineering (14876) Bioinformatics (44392) Biophysics (22588) Cancer Biology (19713) Cell Biology (26896) Clinical Trials (138) Developmental Biology (13961) Ecology (21001) Epidemiology (2067) Evolutionary Biology (25436) Genetics (16162) Genomics (23498) Immunology (18693) Microbiology (42451) Molecular Biology (18054) Neuroscience (93419) Paleontology (700) Pathology (2977) Pharmacology and Toxicology (5092) Physiology (8111) Plant Biology (15997) Scientific Communication and Education (2095) Synthetic Biology (4559) Systems Biology (10233) Zoology (2389)
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