Parameters for forward simulation using serosim [38].

Parameters for forward simulation using serosim [38].

<p>Unless otherwise noted all parameters are drawn from log-normal distributions.</p> <p>(XLSX)</p>

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Bibliografski detalji
Glavni autor:	Marco Hamins-Puértolas (15238661) (author)
Daljnji autori:	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)
Izdano:	2025
Teme:	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
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Slični predmeti

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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

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

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

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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (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.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

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

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

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

Relative observational noise by assay and dataset.
od: Marco Hamins-Puértolas (15238661)
Izdano: (2025)

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

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

الترتيبات القانونية الخاصة بالنظام النقدي الدولي ومدى استجابتها لمصالح المجتمع الدولي
od: Mahafzah , Emran
Izdano: (2021)

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

Top-performing model configuration and performance by algorithm type.
od: Uğur Ejder (22683228)
Izdano: (2025)

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

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

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

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

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

The most effective active substances for subfertility treatment.
od: Uğur Ejder (22683228)
Izdano: (2025)

The comparison of the accuracy score of the benchmark and the proposed models.
od: Uğur Ejder (22683228)
Izdano: (2025)

The prediction accuracy of simple versus hybrid models.
od: Uğur Ejder (22683228)
Izdano: (2025)

The accuracy score change matrix following model development.
od: Uğur Ejder (22683228)
Izdano: (2025)

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

Flow diagram of the proposed model.
od: Uğur Ejder (22683228)
Izdano: (2025)

Flow diagram of the conversion of drugs into active substances.
od: Uğur Ejder (22683228)
Izdano: (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>.
od: Sophia David (4172902)
Izdano: (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).
od: Sophia David (4172902)
Izdano: (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.
od: Sophia David (4172902)
Izdano: (2025)

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

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

Overview of the Illumina paired-end entries available in the International Nucleotide Sequence Database Collaboration (INSDC) databases as of 31 March 2025.
od: Sophia David (4172902)
Izdano: (2025)

Curated list of AMR mechanisms by pathogen from AMRFinderPlus that are reported in amr.watch.
od: Sophia David (4172902)
Izdano: (2025)

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