About: Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning

Facets (new session)
Description
Metadata
Settings
- owl:sameAs
- Inference Rule:

About: Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
title	Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning
Creator	Song, Yun Marson, Alexander Mowery, Cody Ye, Chun
source	MedRxiv
abstract	Mitigating transmission of SARS-CoV-2 has been complicated by the inaccessibility and, in some cases, inadequacy of testing options to detect present or past infection. Immunochromatographic lateral flow assays (LFAs) are a cheap and scalable modality for tracking viral transmission by testing for serological immunity, though systematic evaluations have revealed the low performance of some SARS-CoV-2 LFAs. Here, we re-analyzed existing data to present a proof-of-principle machine learning framework that may be used to inform the pairing of LFAs to achieve superior classification performance while enabling tunable False Positive Rates optimized for the estimated seroprevalence of the population being tested.
has issue date	2020-07-16(xsd:dateTime)
bibo:doi	10.1101/2020.07.15.20154773
has license	medrxiv
sha1sum (hex)	774f2c22356e3d2a1d0f164b436fd849c786eb73
schema:url	https://doi.org/10.1101/2020.07.15.20154773
resource representing a document's title	Improved COVID-19 Serology Test Performance by Integrating Multiple Lateral Flow Assays using Machine Learning
resource representing a document's body	covid:774f2c22356e3d2a1d0f164b436fd849c786eb73#body_text
is schema:about of	named entity 'immunity' named entity 'cases' named entity 'BEING' named entity 'SEROPREVALENCE' named entity 'USED' »more»

Faceted Search & Find service v1.13.91 as of Mar 24 2020

Alternative Linked Data Documents: Sponger | ODE Content Formats:

RDF

ODATA

Microdata

About

OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software