Sarah Filippi
Overview
Explore the profile of Sarah Filippi including associated specialties, affiliations and a list of published articles.
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Articles
29
Citations
661
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0
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Recent Articles
1.
Lhoste V, Zhou B, Mishra A, Bennett J, Filippi S, Asaria P, et al.
Nat Cardiovasc Res
. 2024 Aug;
3(1):94.
PMID: 39195902
No abstract available.
2.
Cuthbertson L, Lober U, Ish-Horowicz J, McBrien C, Churchward C, Parker J, et al.
Commun Biol
. 2024 Feb;
7(1):171.
PMID: 38347162
Microbial communities at the airway mucosal barrier are conserved and highly ordered, in likelihood reflecting co-evolution with human host factors. Freed of selection to digest nutrients, the airway microbiome underpins...
3.
Lhoste V, Zhou B, Mishra A, Bennett J, Filippi S, Asaria P, et al.
Nat Cardiovasc Res
. 2024 Feb;
3(1):46-59.
PMID: 38314318
Cardiovascular and renal conditions have both shared and distinct determinants. In this study, we applied unsupervised clustering to multiple rounds of the National Health and Nutrition Examination Survey from 1988...
4.
Bodinier B, Filippi S, Nost T, Chiquet J, Chadeau-Hyam M
J R Stat Soc Ser C Appl Stat
. 2023 Dec;
72(5):1375-1393.
PMID: 38143734
Stability selection represents an attractive approach to identify sparse sets of features jointly associated with an outcome in high-dimensional contexts. We introduce an automated calibration procedure via maximisation of an...
5.
Bodinier B, Vuckovic D, Rodrigues S, Filippi S, Chiquet J, Chadeau-Hyam M
Bioinformatics
. 2023 Oct;
39(11).
PMID: 37847776
Motivation: In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus...
6.
Komodromos M, Aboagye E, Evangelou M, Filippi S, Ray K
Bioinformatics
. 2022 Jun;
38(16):3918-3926.
PMID: 35751586
Motivation: Few Bayesian methods for analyzing high-dimensional sparse survival data provide scalable variable selection, effect estimation and uncertainty quantification. Such methods often either sacrifice uncertainty quantification by computing maximum a...
7.
Reiker T, Golumbeanu M, Shattock A, Burgert L, Smith T, Filippi S, et al.
Nat Commun
. 2021 Dec;
12(1):7212.
PMID: 34893600
Individual-based models have become important tools in the global battle against infectious diseases, yet model complexity can make calibration to biological and epidemiological data challenging. We propose using a Bayesian...
8.
Frainay C, Pitarch Y, Filippi S, Evangelou M, Custovic A
Clin Exp Allergy
. 2021 Jul;
51(9):1185-1194.
PMID: 34213816
Background: Biomedical research increasingly relies on computational approaches to extract relevant information from large corpora of publications. Objective: To investigate the consequence of the ambiguity between the use of terms...
9.
Monod M, Blenkinsop A, Xi X, Hebert D, Bershan S, Tietze S, et al.
Science
. 2021 Feb;
371(6536).
PMID: 33531384
After initial declines, in mid-2020 a resurgence in transmission of novel coronavirus disease (COVID-19) occurred in the United States and Europe. As efforts to control COVID-19 disease are reintensified, understanding...
10.
Accelerated MRI-predicted brain ageing and its associations with cardiometabolic and brain disorders
Kolbeinsson A, Filippi S, Panagakis Y, Matthews P, Elliott P, Dehghan A, et al.
Sci Rep
. 2020 Nov;
10(1):19940.
PMID: 33203906
Brain structure in later life reflects both influences of intrinsic aging and those of lifestyle, environment and disease. We developed a deep neural network model trained on brain MRI scans...