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1. A Bayesian non-parametric mixed-effects model of microbial growth curves

   https://doi.org/10.1371/journal.pcbi.1008366  

   Tonner, Peter D. ; Darnell, Cynthia L. ; Bushell, Francesca M. ; Lund, Peter A. ; Schmid, Amy K. ; Schmidler, Scott C. ( October 2020 , PLOS Computational Biology)

   Papin, Jason A. (Ed.)

   Substantive changes in gene expression, metabolism, and the proteome are manifested in overall changes in microbial population growth. Quantifying how microbes grow is therefore fundamental to areas such as genetics, bioengineering, and food safety. Traditional parametric growth curve models capture the population growth behavior through a set of summarizing parameters. However, estimation of these parameters from data is confounded by random effects such as experimental variability, batch effects or differences in experimental material. A systematic statistical method to identify and correct for such confounding effects in population growth data is not currently available. Further, our previous work has demonstrated that parametric models are insufficient to explain and predict microbial response under non-standard growth conditions. Here we develop a hierarchical Bayesian non-parametric model of population growth that identifies the latent growth behavior and response to perturbation, while simultaneously correcting for random effects in the data. This model enables more accurate estimates of the biological effect of interest, while better accounting for the uncertainty due to technical variation. Additionally, modeling hierarchical variation provides estimates of the relative impact of various confounding effects on measured population growth. 

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2. Assessing the Impact of Precision Parameter Prior in Bayesian Non-parametric Growth Curve Modeling

   https://doi.org/10.3389/fpsyg.2021.624588  

   Tong, Xin ; Ke, Zijun ( March 2021 , Frontiers in Psychology)

   null (Ed.)

   Bayesian non-parametric (BNP) modeling has been developed and proven to be a powerful tool to analyze messy data with complex structures. Despite the increasing popularity of BNP modeling, it also faces challenges. One challenge is the estimation of the precision parameter in the Dirichlet process mixtures. In this study, we focus on a BNP growth curve model and investigate how non-informative prior, weakly informative prior, accurate informative prior, and inaccurate informative prior affect the model convergence, parameter estimation, and computation time. A simulation study has been conducted. We conclude that the non-informative prior for the precision parameter is less preferred because it yields a much lower convergence rate, and growth curve parameter estimates are not sensitive to informative priors. 

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3. Estimation for nonignorable missing response or covariate using semi-parametric quantile regression imputation and a parametric response probability model

   https://doi.org/10.5705/ss.202020.0053  

   Berg, Emily ; Yu, Cindy ( January 2022 , Statistica Sinica)

   null (Ed.)
4. Non-parametric and semi-parametric support estimation using SEquential RESampling random walks on biomolecular sequences

   https://doi.org/10.1186/s13015-020-00167-0  

   Wang, Wei ; Smith, Jack ; Hejase, Hussein A. ; Liu, Kevin J. ( December 2020 , Algorithms for Molecular Biology)
5. What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance: What makes long-term monitoring convenient? A parametric analysis of value of information in infrastructure maintenance

   https://doi.org/10.1002/stc.2329  

   Li, Shuo ; Pozzi, Matteo ( May 2019 , Structural Control and Health Monitoring)