Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.

Simulated serological data using serosim and subsequent results using the full mixture model on (a) HAI, (b) IgG and (c) IgM data with additional observational noise.

<p>For each we present the predicted infection status using the mixture model approach along with the true underlying infection status. Parameters chosen for forward simulation are presented in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1013708#pcbi.1013...

Volledige beschrijving

Bewaard in:

Bibliografische gegevens
Hoofdauteur:	Marco Hamins-Puértolas (15238661) (author)
Andere auteurs:	Darunee Buddhari (625855) (author), Henrik Salje (589157) (author), Angkana T. Huang (12262139) (author), Taweewun Hunsawong (798675) (author), Derek A.T. Cummings (14971341) (author), Stefan Fernandez (146590) (author), Aaron Farmer (4288318) (author), Surachai Kaewhiran (22683535) (author), Direk Khampaen (22683538) (author), Anon Srikiatkhachorn (110184) (author), Sopon Iamsirithaworn (260152) (author), Adam Waickman (118668) (author), Stephen J. Thomas (7105673) (author), Timothy Endy (150971) (author), Alan L. Rothman (6835178) (author), Kathryn B. Anderson (6835166) (author), Isabel Rodriguez-Barraquer (233670) (author)
Gepubliceerd in:	2025
Onderwerpen:	Medicine Biotechnology Ecology Immunology Cancer Science Policy Infectious Diseases Virology Computational Biology Biological Sciences not elsewhere classified Mathematical Sciences not elsewhere classified Chemical Sciences not elsewhere classified since many cases linked immunosorbent assay independently utilize information hemagglutination inhibition assay globe &# 8217 currently implemented cut convalescent serum collected 87 &# 8211 82 &# 8211 real world data multiple serological assays infer infection events generate finalized interpretations bayesian mixture model prior standard interpretations pcr confirmed infections jointly model data gold standard ). f1 scores ). hai data consistently direct detection methods gold standard f1 scores infection detection eia ). including data individual assays new model igm data whether individuals transmission potential results provide recently experienced provide insight probabilistic framework population living point approaches pathogen systems kamphaeng phet interpreting results incorporating igg important role higher accuracy first test diagnostic process creating uncertainty combined metric already half 677 pairs
Tags:	Voeg label toe Geen labels, Wees de eerste die dit record labelt!

Gelijkaardige items

Comparison between simulation based estimates for boosts in GM HAI, IgG, and IgM that is experienced after an infection event as a function of acute GM HAI, IgG, and IgM titers individuals under the varying levels of observational noise.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Parameters for forward simulation using serosim [38].
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Distribution of paired serological data that fall into probability buckets when using Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Acute and convalescent samples from cohort study data for serological assays, colored by the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent serum samples.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Acute and convalescent data for serological assays, colored by probability of infection found during model testing for Model 9. a) Geometric mean titers of a haemagglutination inhibition assay (GM HAI) for all four serotypes of dengue virus. b) Immunoglobulin G (IgG) c) Immunoglobulin M (IgM) d) Ratio of IgM to IgG at both acute and convalescent sera samples.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Relationship between estimated probability of a dengue virus infection (DENV) found during model testing for Model 9 (incorporating paired IgG, IgM, GM HAI, and acute GM HAI serological data) and the number of serological assays (RT-PCR, HAI, and EIA) that interpret the individual as having an infection.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Relative observational noise by assay and dataset.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Comparison of each accuracy metric (sensitivity, specificity, F1, and area under the curve for reporter operator curve (AUC ROC)) on each model. Models are defined by which assays were incorporated (i.e., HAI, IgG, and/or IgM). Bolded values represent the best performing model for the infection definition and metric of interest.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Model comparisons for simulated data with differing amounts of observational noise.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Comparison of accuracy metrics (sensitivity, specificity, and AUC ROC) of each model defined by what subset of data was used for training and testing.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Acute and convalescent samples from cohort study data for serological assays, colored by whether they are predicted as infections using Model 9.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Acute and convalescent serological data from Thai cohorts used in this analyses, colored by true infections status and prediction of infection during model testing.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Characteristics for Kamphaeng Phet cohort studies split by source (KPS1 and KFCS) as well as across the entire dataset. Information on age, hospitalization as well as serological assay results for RT-PCR, haemagglutination inhibition assay (HAI), and enzyme immunoassay (EIA) are presented.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Theoretical relationship between time since symptom onset and titers in one infected (red) and three uninfected (blue) individuals.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

(A) Estimated intercept for titer boost when acute titers are initially low for each assay.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Parameters found in Bayesian estimation framework, definition, and prior distribution.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

Acute and convalescent data for serological assays, colored by probability of.
door: Marco Hamins-Puértolas (15238661)
Gepubliceerd in: (2025)

A Four-Gene Signature from Blood to Exclude Bacterial Etiology of Lower Respiratory Tract Infection in Adults
door: Edward E. Walsh (20760337)
Gepubliceerd in: (2025)

الترتيبات القانونية الخاصة بالنظام النقدي الدولي ومدى استجابتها لمصالح المجتمع الدولي
door: Mahafzah , Emran
Gepubliceerd in: (2021)

Data Sheet 1_Whole genome sequencing as a reliable alternative for Salmonella serotyping: a comparative study with the gold-standard method.pdf
door: Irene Mileto (20877575)
Gepubliceerd in: (2025)

Top-performing model configuration and performance by algorithm type.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Comparison of baseline and hybrid machine learning models in predicting IVF outcomes (%).
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The list of parameters of the modified data set for machine learning (<i>n</i> = 162).
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The statistical description of the original data set of the patients (<i>n</i> = 162).
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Confusion matrix of the ABC–LR–RF hybrid model for IVF outcome prediction.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

ROC and PR–AUC curves of the ABC–LR–RF hybrid model for IVF outcome prediction.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The most effective active substances for subfertility treatment.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The comparison of the accuracy score of the benchmark and the proposed models.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The prediction accuracy of simple versus hybrid models.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

The accuracy score change matrix following model development.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Calibration curve of the ABC–LR–RF hybrid model for IVF outcome prediction.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Flow diagram of the proposed model.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Flow diagram of the conversion of drugs into active substances.
door: Uğur Ejder (22683228)
Gepubliceerd in: (2025)

Validation of ultrasound assessments across BMI groups using same-day ultrasound and radiography.
door: JoJo Yiying Zou (22676490)
Gepubliceerd in: (2025)

Scoliosis assessment in this study: A) ultrasound scan using the Scolioscan® system; B) standard posteroanterior radiograph showing a Cobb angle of 25.6°; C) corresponding coronal ultrasound image showing a curvature angle of 26.9° using SP method.
door: JoJo Yiying Zou (22676490)
Gepubliceerd in: (2025)

<i>K. pneumoniae</i> genomes represented in amr.watch as of 31 March 2025, filtered by the presence of the carbapenem resistance gene, <i>bla</i><sub>NDM-5</sub>.
door: Sophia David (4172902)
Gepubliceerd in: (2025)

<i>K. pneumoniae</i> genomes represented in amr.watch as of 31 March 2025 from the USA (A) and China (B) filtered by the most frequent ST from each country (ST258 and ST11, respectively).
door: Sophia David (4172902)
Gepubliceerd in: (2025)

Proportion of genomes by year from the five critical-level priority pathogens carrying one or more AMR mechanisms for relevant antimicrobial classes, as displayed in the “AMR mechanism proportion over time” panel within amr.watch.
door: Sophia David (4172902)
Gepubliceerd in: (2025)

Distribution of pathogen genomes represented in amr.watch as of 31 March 2025.
door: Sophia David (4172902)
Gepubliceerd in: (2025)

The interactive amr.watch application here shows an overview from 179,518 <i>E. coli</i> genomes available as of 31 March 2025.
door: Sophia David (4172902)
Gepubliceerd in: (2025)

Cannot write session to /tmp/vufind_sessions/sess_36q9dp3en4m1t0ag7m0fa4bl1h