Conference Paper, 2024
Classification of Patients With Idiopathic Pulmonary Fibrosis According to Blood Biomarker Signatures by Consensus Cluster Analysis: A Multiple Machine Learning Approach
A13. TOWARDS IMPLEMENTATION: NEW BIOMARKERS IN ILD,
Page a1007,
10.1164/ajrccm-conference.2024.209.1_meetingabstracts.a1007
Contributors
Fainberg, Hernan P
0000-0001-9330-9047
[1]
Moodley, Yuben P
0000-0002-4119-1734
[2]
Triguero, Isaac
0000-0002-0150-0651
[3]
Corte, Tamera Jo
[4]
Sand, Jannie Marie Bülow
0000-0002-3239-0934
[5]
Leeming, Diana Julie
[5]
Karsdal, Morten Asser
0000-0002-4764-5100
[5]
Renzoni, Elisabetta Augusta
0000-0002-1118-797X
[6]
Wells, Athol Umfrey
[6]
Fahy, William A
0000-0002-1018-0848
[7]
Oballa, Eunice
[7]
Porte, Joanne
[8]
Saini, Gauri
0000-0002-7786-737X
[8]
Johnson, Simon Richard
0000-0002-9837-2763
[8]
Wain, Louise V
0000-0003-4951-1867
[9]
Molyneaux, Philip L
0000-0003-1301-8800
[1]
Maher, Toby M
0000-0001-7192-9149
[1]
Stewart, Iain D
0000-0002-1340-2688
[1]
Jenkins, Richard Gisli
0000-0002-7929-2119
[1]
Affiliations
- [1]
Imperial College London
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [2]
University of Western Australia
[NORA names:
Australia; Oceania; OECD];
- [3]
University of Granada
[NORA names:
Spain; Europe, EU; OECD];
- [4]
The University of Sydney
[NORA names:
Australia; Oceania; OECD];
- [5]
Nordic Bioscience (Denmark)
[NORA names:
Nordic Bioscience; Private Research; Denmark; Europe, EU; Nordic; OECD];
(... more)
- [6]
Royal Brompton Hospital
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [7]
GlaxoSmithKline (United Kingdom)
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [8]
University of Nottingham
[NORA names:
United Kingdom; Europe, Non-EU; OECD];
- [9]
University of Leicester
[NORA names:
United Kingdom; Europe, Non-EU; OECD]
(less)
Keywords
analysis,
approach,
biomarker signatures,
blood,
classification,
classification of patients,
cluster analysis,
consensus,
consensus clustering analysis,
fibrosis,
idiopathic pulmonary fibrosis,
learning approach,
machine learning approach,
multiple machine learning approaches,
patients,
pulmonary fibrosis,
signature
Data Provider: Digital Science