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Discovery of sparse, reliable omic biomarkers with Stabl.
Hédou, Julien; Maric, Ivana; Bellan, Grégoire; Einhaus, Jakob; Gaudillière, Dyani K; Ladant, Francois-Xavier; Verdonk, Franck; Stelzer, Ina A; Feyaerts, Dorien; Tsai, Amy S; Ganio, Edward A; Sabayev, Maximilian; Gillard, Joshua; Amar, Jonas; Cambriel, Amelie; Oskotsky, Tomiko T; Roldan, Alennie; Golob, Jonathan L; Sirota, Marina; Bonham, Thomas A; Sato, Masaki; Diop, Maïgane; Durand, Xavier; Angst, Martin S; Stevenson, David K; Aghaeepour, Nima; Montanari, Andrea; Gaudillière, Brice.
Affiliation
  • Hédou J; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Maric I; Department of Pediatrics, Stanford University, Stanford, CA, USA.
  • Bellan G; Télécom Paris, Institut Polytechnique de Paris, Paris, France.
  • Einhaus J; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Gaudillière DK; Department of Pathology and Neuropathology, University Hospital and Comprehensive Cancer Center Tübingen, Tübingen, Germany.
  • Ladant FX; Division of Plastic and Reconstructive Surgery, Department of Surgery, Stanford University, Stanford, CA, USA.
  • Verdonk F; Department of Economics, Harvard University, Cambridge, MA, USA.
  • Stelzer IA; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Feyaerts D; Sorbonne University, GRC 29, AP-HP, DMU DREAM, Department of Anesthesiology and Intensive Care, Hôpital Saint-Antoine, Assistance Publique-Hôpitaux de Paris, Paris, France.
  • Tsai AS; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Ganio EA; Department of Pathology, University of California San Diego, La Jolla, CA, USA.
  • Sabayev M; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Gillard J; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Amar J; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Cambriel A; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Oskotsky TT; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Roldan A; Department of Medical BioSciences, Radboud University Medical Center, Nijmegen, The Netherlands.
  • Golob JL; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Sirota M; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Bonham TA; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
  • Sato M; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
  • Diop M; Department of Medicine, University of Michigan Medical School, Ann Arbor, MI, USA.
  • Durand X; Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, CA, USA.
  • Angst MS; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Stevenson DK; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Aghaeepour N; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
  • Montanari A; École Polytechnique, Institut Polytechnique de Paris, Paris, France.
  • Gaudillière B; Department of Anesthesiology, Perioperative & Pain Medicine, Stanford University, Stanford, CA, USA.
Nat Biotechnol ; 2024 Jan 02.
Article in En | MEDLINE | ID: mdl-38168992
ABSTRACT
Adoption of high-content omic technologies in clinical studies, coupled with computational methods, has 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 method 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 1,400-35,000 features down to 4-34 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 pre-term birth and a pre-operative immune signature of post-surgical infections. Stabl is available at https//github.com/gregbellan/Stabl .
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Nat Biotechnol / Nat. biotechnol / Nature biotechnology Journal subject: BIOTECNOLOGIA Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States
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Full text: 1 Collection: 01-internacional Database: MEDLINE Type of study: Prognostic_studies Language: En Journal: Nat Biotechnol / Nat. biotechnol / Nature biotechnology Journal subject: BIOTECNOLOGIA Year: 2024 Document type: Article Affiliation country: United States Country of publication: United States