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SciClone Inc sciclone's beta mixture model
Sciclone's Beta Mixture Model, supplied by SciClone Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
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Article Title: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
Article Snippet: We report the results with two clustering methods in SciClone: Beta mixture model (BMM, default) and Beta-binomial mixture model (Binomial BMM).

Article Title: Reconstructing evolutionary trajectories of mutation signature activities in cancer using TrackSig
Article Snippet: For this comparison, we used two different noise models with SciClone: the binomial-beta model which is an exact match to how the simulated data are generated, and the beta model, which is not and which was the model used for computing the reconstruction error above.

Article Title: Increased number of subclones in lung squamous cell carcinoma elicits overexpression of immune related genes
Article Snippet: There are several statistical models for the inference of clonal population structure in a tumor, including SciClone’s ( 26 ) variational Bayesian mixture model, MAFtools’ ( 29 ) Gaussian mixture, and PyClone’s ( 30 ) beta-binomial emission densities.

Article Title: A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA
Article Snippet: Sciclone uses a hierarchical Bayesian mixture model to infer subclonal populations ( Miller et al., 2014 ).

Variant Assay:

Article Title: Methods and systems for determining somatic mutation clonality
Article Snippet: .. One existing method, SciClone, uses the variational mixture model to determine variant allele frequency by clustering VAFs and determining the probabilities of variants belonging to one or more clusters. .. However, the clustering of SciClone does not account for the average copy number at the mutation locus, the copy number of the variant allele, or the tumor purity level.



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Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

Journal: Archives of Public Health

Article Title: Early warning of hepatitis B epidemics in Henan Province, China, from 2014 to 2023 based on Baidu Index and Bayesian Structural Time Series model

doi: 10.1186/s13690-026-01837-y

Figure Lengend Snippet: Comparison of 12-month and 33-month ahead forecasts for monthly Hepatitis B incidence in Henan Province, China, 2014–2023, using seasonal autoregressive integrated moving average, seasonal autoregressive integrated moving average with exogenous variables, Bayesian structural time series without Baidu Index, and Bayesian structural time series with Baidu Index models. A Seasonal autoregressive integrated moving average model for 12-month ahead forecasts. B Seasonal autoregressive integrated moving average with exogenous variables model for 12-month ahead forecasts. C Bayesian structural time series model without Baidu Index for 12-month ahead forecasts. D Bayesian structural time series model with Baidu Index for 12-month ahead forecasts. E Seasonal autoregressive integrated moving average model for 33-month ahead forecasts. F Seasonal autoregressive integrated moving average with exogenous variables model for 33-month ahead forecasts. G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts. H Bayesian structural time series model with Baidu Index for 33-month ahead forecasts. The Bayesian structural time series models, especially with Baidu Index, demonstrate superior predictive performance

Article Snippet: G Bayesian structural time series model without Baidu Index for 33-month ahead forecasts.

Techniques: Comparison