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Unsupervised Machine Learning Identifies Asthma Phenotypes in the Population‐Based West Sweden Asthma Study

Year: 2025

Type: article

Abstract: Non peer reviewed

Source: Clinical & Experimental Allergy

DOI: 10.1111/cea.70161

Authors: Muwada Bashir Awad Bashir, Daniil Lisik, Saliha Selin Ozuygur Ermis, Rani Basna, Reshed Abohalaka, Selin Ercan, Helena Backman, Teet Pullerits, Roxana Mincheva, Göran Wennergren, Madeleine Rådinger, Jan Lötvall, Linda Ekerljung, Hannu Kankaanranta, Bright I. Nwaru

Institutions: University of Gothenburg, Umeå University, Lund University, Sahlgrenska University Hospital, Tampere University, Seinäjoen Keskussairaala, University of Tampere, Wallenberg Centre for Molecular and Translational Medicine

Language: English

Topic: Asthma and respiratory diseases

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: hybrid

Estimated APC paid: $4,760

Funders: Hjärt-Lungfonden, Vetenskapsrådet, NordForsk, Astma- och Allergiförbundet

Awards: G2297162763, G2931921215, G7813144114, Respiratory Epidemiology Research Platform (STELLAR): harnessing cohort-register linked data to catalyze novel investigations in obstructive airway diseases

OpenAlex ID: W4415209546

FWCI: 0.548

Cites: 7

Cited by: 1

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