Machine Learning–Based Deep Phenotyping of Atopic Dermatitis

2021 ◽  
Author(s):  
Laura Maintz ◽  
Thomas Welchowski ◽  
Nadine Herrmann ◽  
Juliette Brauer ◽  
Anna Sophie Kläschen ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atopic Dermatitis ◽  
Deep Phenotyping

scholarly journals LB727 Using a machine learning approach to identify rare and low-frequency filaggrin variants associated with remission of Atopic Dermatitis

2021 ◽  
Vol 141 (9) ◽  
pp. B6
Author(s):  
R. Berna ◽  
N. Mitra ◽  
O. Hoffstad ◽  
B. Wubbenhorst ◽  
K. Nathanson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atopic Dermatitis ◽  
Low Frequency ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals Machine learning applied to atopic dermatitis transcriptome reveals distinct therapy-dependent modification of the keratinocyte immunophenotype

2019 ◽  
Author(s):  
K. Clayton ◽  
A. Vallejo ◽  
S. Sirvent ◽  
J. Davies ◽  
G. Porter ◽  
...  
Keyword(s):  
Gene Expression ◽  
Machine Learning ◽  
T Cells ◽  
Atopic Dermatitis ◽  
Skin Inflammation ◽  
Response To Treatment ◽  
Cutaneous Disease ◽  
Inflammatory Conditions ◽  
Inflammatory Stimuli ◽  
Dependent Modification

AbstractBackgroundAtopic dermatitis (AD) arises from a complex interaction between an impaired epidermal barrier, environmental exposures, and the infiltration of Th1/Th2/Th17/Th22 T cells. Transcriptomic analysis has advanced understanding of gene expression in cells and tissues. However, molecular quantitation of cytokine transcripts does not predict the importance of a specific pathway in AD or cellular responses to different inflammatory stimuli.ObjectiveTo understand changes in keratinocyte transcriptomic programmes in human cutaneous disease during development of inflammation and in response to treatment.MethodsWe performed in silico deconvolution of the whole-skin transcriptome. Using co-expression clustering and machine learning tools, we resolved the gene expression of bulk skin (n=7 datasets, n=406 samples), firstly, into unsupervised keratinocyte immune response phenotypes and, secondly, into 19 cutaneous cell signatures of purified populations from publicly available datasets.ResultsWe identify three unique transcriptomic programmes in keratinocytes, KC1, KC2, KC17, characteristic to immune signalling from disease-associated helper T cells. We cross-validate those signatures across different skin inflammatory conditions and disease stages and demonstrate that the keratinocyte response during treatment is therapy dependent. Broad spectrum treatment with ciclosporin ameliorated the KC17 response in AD lesions to a non-lesional immunophenotype, without altering KC2. Conversely, the specific anti-Th2 therapy, dupilumab, reversed the KC2 immunophenotype.ConclusionOur analysis of transcriptomic signatures in cutaneous disease biopsies reveals the complexity of keratinocyte programming in skin inflammation and suggests that the perturbation of a single axis of immune signal alone may be insufficient to resolve keratinocyte immunophenotype abnormalities.

DEEP PHENOTYPING AND PREDICTION OF LONG-TERM HEART FAILURE BY MACHINE LEARNING

2019 ◽  
Vol 73 (9) ◽  
pp. 690
Author(s):  
Xiaodong Zhuang ◽  
Xiuting Sun ◽  
Xiangbin Zhong ◽  
Huimin Zhou ◽  
Shaozhao Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Heart Failure ◽  
Deep Phenotyping

scholarly journals Using a machine learning approach to identify low-frequency and rare filaggrin alleles associated with remission of atopic dermatitis

2021 ◽  
pp. 100046
Author(s):  
Ronald Berna ◽  
Nandita Mitra ◽  
Ole Hoffstad ◽  
Bradley Wubbenhorst ◽  
Katherine L. Nathanson ◽  
...  
Keyword(s):  
Machine Learning ◽  
Atopic Dermatitis ◽  
Low Frequency ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals DEEP PHENOTYPING USING UNSUPERVISED MACHINE LEARNING OF HFPEF PATIENTS WITH DIABETES MELLITUS IN THE TOPCAT AMERICAS COHORT

2021 ◽  
Vol 77 (18) ◽  
pp. 890
Author(s):  
Ian Atkinson ◽  
Kabir Al-Tariq ◽  
Gauri Dandi ◽  
Nuha Gani ◽  
Zyannah Mallick ◽  
...  
Keyword(s):  
Diabetes Mellitus ◽  
Machine Learning ◽  
Unsupervised Machine Learning ◽  
Patients With Diabetes ◽  
Deep Phenotyping

scholarly journals DEEP PHENOTYPING BY UNSUPERVISED MACHINE LEARNING OF PARTICIPANTS IN THE BARI 2D TRIAL

2021 ◽  
Vol 77 (18) ◽  
pp. 1601
Author(s):  
Gauri Dandi ◽  
Nuha Gani ◽  
Zyannah Mallick ◽  
Ian Atkinson ◽  
Kabir Al-Tariq ◽  
...  
Keyword(s):  
Machine Learning ◽  
Unsupervised Machine Learning ◽  
Deep Phenotyping
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