EDP Sciences logo
Authentification abonné
Rechercher
Menu
Accueil Tous les numéros Volume 39 (Novembre 2023) Hors série n° 1 Med Sci (Paris), 39 (2023) 22-27 Références
Accès gratuit
Numéro
Med Sci (Paris)
Volume 39, Novembre 2023
Les Cahiers de Myologie
Page(s) 22 - 27
Section Prix SFM
DOI https://doi.org/10.1051/medsci/2023136
Publié en ligne 17 novembre 2023
  1. Hu T, Chitnis N, Monos D, et al. Next-generation sequencing technologies: An overview. Hum Immunol 2021; 82 : 801–11. [CrossRef] [PubMed] [Google Scholar]
  2. Gorokhova S, Biancalana V, Lévy N, et al. Clinical massively parallel sequencing for the diagnosis of myopathies. Rev Neurol (Paris) 2015 ; 171 : 558–571. [CrossRef] [PubMed] [Google Scholar]
  3. Logsdon GA, Vollger MR, Eichler EE. Long-read human genome sequencing and its applications. Nat Rev Genet 2020; 21 : 597–614. [CrossRef] [PubMed] [Google Scholar]
  4. Mehrmohamadi M, Sepehri MH, Nazer N, et al. A comparative overview of epigenomic profiling methods. Front Cell Dev Biol 2021; 9 : 714687. [CrossRef] [PubMed] [Google Scholar]
  5. Li X, Wang CY. From bulk, single-cell to spatial RNA sequencing. Int J Oral Sci 2021; 13 : 36. [CrossRef] [PubMed] [Google Scholar]
  6. Rosenberg AB, Roco CM, Muscat RA, et al. Single-cell profiling of the developing mouse brain and spinal cord with split-pool barcoding. Science 2018 ; 360 : 176–182. [NASA ADS] [CrossRef] [PubMed] [Google Scholar]
  7. Aebersold R, Mann M. Mass-spectrometric exploration of proteome structure and function. Nature 2016 ; 537 : 347–355. [CrossRef] [PubMed] [Google Scholar]
  8. Smith LM, Agar JN, Chamot-Rooke J et al. The human proteoform project: Defining the human proteome. Sci Adv 2021; 46 : eabk0734. [CrossRef] [PubMed] [Google Scholar]
  9. Rozanova S, Barkovits K, Nikolov M et al. Quantitative mass spectrometry-based proteomics: An overview. Methods Mol Biol 2021; 2228 : 85–116. [CrossRef] [PubMed] [Google Scholar]
  10. Aderemi AV, Ayeleso AO, Oyedapo OO, et al. Metabolomics: A scoping review of its role as a tool for disease biomarker discovery in selected non-communicable diseases. Metabolites 2021; 11 : 418. [CrossRef] [PubMed] [Google Scholar]
  11. Wishart DS. Metabolomics for investigating physiological and pathophysiological processes. Physiol Rev 2019 ; 99 : 1819–1875. [CrossRef] [PubMed] [Google Scholar]
  12. Wang R, Li B, Lam SM, et al. Integration of lipidomics and metabolomics for in-depth understanding of cellular mechanism and disease progression. J Genet Genomics 2020; 47 : 69–83. [CrossRef] [PubMed] [Google Scholar]
  13. Karczewski KJ, Snyder MP. Integrative omics for health and disease. Nat Rev Genet 2018 ; 19 : 299–310. [CrossRef] [PubMed] [Google Scholar]
  14. Subramanian I, Verma S, Kumar S, et al. Multi-omics data integration, interpretation, and its application. Bioinforma Biol Insights 2020; 14 : 1177932219899051. [CrossRef] [Google Scholar]
  15. Argelaguet R, Velten B, Arnol D, et al. Multi-omics factor analysis–a framework for unsupervised integration of multi-omics data set. Mol Syst Biol 2018 ; 14 : e8124. [CrossRef] [PubMed] [Google Scholar]
  16. Kang M, Ko E, Mersha TB A roadmap for multi-omics data integration using deep learning. Brief Bioinform 2022; 23 : bbab454. [CrossRef] [PubMed] [Google Scholar]
  17. Bodein A, Scott-Boyer MP, Perin O et al. Interpretation of network-based integration from multi-omics longitudinal data. Nucleic Acids Res 2022; 50 : e27. [CrossRef] [PubMed] [Google Scholar]
  18. Brière G, E. Darbo É, P. Thébault P, et al. Consensus clustering applied to multi-omics disease subtyping. BMC Bioinformatics 2021; 22 : 361. [CrossRef] [PubMed] [Google Scholar]
  19. Eng SWM, Olazagasti JM, Goldenberg A, et al. A clinically and biologically based subclassification of the idiopathic inflammatory myopathies using machine learning. ACR Open Rheumatol 2020; 2 : 158–66. [CrossRef] [PubMed] [Google Scholar]
  20. Danieli MG, Tonacci A, Paladini A, et al. A machine learning analysis to predict the response to intravenous and subcutaneous immunoglobulin in inflammatory myopathies. A proposal for a future multi-omics approach in autoimmune diseases. Autoimmun Rev 2022; 21 : 103105. [CrossRef] [Google Scholar]
  21. Heier CR, Zhang A, Nguyen NY, et al. Multi-omics identifies circulating miRNA and protein biomarkers for facioscapulohumeral dystrophy. J Pers Med 2020; 10 : 236. [CrossRef] [PubMed] [Google Scholar]
  22. Mitropoulos K, Katsila T, Patrinos GP, et al. Multi-omics for biomarker discovery and target validation in biofluids for amyotrophic lateral sclerosis diagnosis. OMICS 2018 ; 22 : 52–64. [CrossRef] [PubMed] [Google Scholar]
  23. Djeddi S, Reiss D, Menuet A, et al. Multi-omics comparisons of different forms of centronuclear myopathies and the effects of several therapeutic strategies. Mol Ther 2021; 29 : 2514–34. [CrossRef] [PubMed] [Google Scholar]
  24. Liu JC, Dong SS, Shen H, et al. Multi-omics research in sarcopenia: Current progress and future prospects. Ageing Res Rev 2022; 76 : 101576. [CrossRef] [PubMed] [Google Scholar]
  25. Mournetas V, Massouridès E, Dupont JB, et al. Myogenesis modelled by human pluripotent stem cells: a multi-omic study of Duchenne myopathy early onset ». J Cachexia Sarcopenia Muscle 2021; 12 : 209–32. [CrossRef] [PubMed] [Google Scholar]
  26. Espinosa-Espinosa J, González-Barriga A, López-Castel A, et al. Deciphering the complex molecular pathogenesis of myotonic dystrophy type 1 through omics studies. Int J Mol Sci 2022; 23 : 1441. [CrossRef] [PubMed] [Google Scholar]
  27. Murakami A, Noda S, Kazuta T, et al. Metabolome and transcriptome analysis on muscle of sporadic inclusion body myositis. Ann Clin Transl Neurol 2022; 9 : 1602–15. [CrossRef] [PubMed] [Google Scholar]

Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.

Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.

Le chargement des statistiques peut être long.