Model checking is a critical part of Bayesian data analysis, yet it remains largely unused in systematic studies. Phylogeny estimation has recently moved into an era of increasingly complex models that simultaneously account for multiple evolutionary processes, the statistical fit of these models to the data has rarely been tested. Here we develop a posterior predictive simulation-based model check for a commonly used multispecies coalescent model, implemented in *BEAST, and apply it to 25 published data sets. We show that poor model fit is detectable in the majority of data sets; that this poor fit can mislead phylogenetic estimation; and that in some cases it stems from processes of inherent interest to systematists. We suggest that as systematists scale up to phylogenomic data sets, which will be subject to a heterogeneous array of evolutionary processes, critically evaluating the fit of models to data is an analytical step that can no longer be ignored.
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http://dx.doi.org/10.1093/sysbio/syt057 | DOI Listing |
ACS Cent Sci
December 2024
Department of Molecular Sciences and Nanosystems, Ca' Foscari University of Venice, Via Torino 155, 30172 Mestre, Italy.
Computational generation of cyclic peptide inhibitors using machine learning models requires large size training data sets often difficult to generate experimentally. Here we demonstrated that sequential combination of Random Forest Regression with the pseudolikelihood maximization Direct Coupling Analysis method and Monte Carlo simulation can effectively enhance the design pipeline of cyclic peptide inhibitors of a tumor-associated protease even for small experimental data sets. Further studies showed that such -evolved cyclic peptides are more potent than the best peptide inhibitors previously developed to this target.
View Article and Find Full Text PDFACS ES T Water
November 2024
Waterborne Disease Prevention Branch, Centers for Disease Control and Prevention, Atlanta, Georgia 30333, United States.
Irrigating fresh produce with contaminated water contributes to the burden of foodborne illness. Identifying fecal contamination of irrigation waters and characterizing fecal sources and associated environmental factors can help inform fresh produce safety and health hazard management. Using two previously collected data sets, we developed and evaluated the performance of logistic regression and conditional random forest models for predicting general and human-specific fecal contamination of ponds in southwest Georgia used for fresh produce irrigation.
View Article and Find Full Text PDFPain Rep
February 2025
Pain Research Institute, Institute of Life Course and Medical Sciences, University of Liverpool, Liverpool, United Kingdom.
Introduction: Pain phenomenology in patients with fibromyalgia syndrome (FMS) shows considerable overlap with neuropathic pain. Altered neural processing leading to symptoms of neuropathic pain can occur at the level of the spinal cord, and 1 potential mechanism is spinal disinhibition. A biomarker of spinal disinhibition is impaired H-reflex rate-dependent depression (HRDD).
View Article and Find Full Text PDFSurv Geophys
April 2024
Department of Atmospheric and Oceanic Science, University of Wisconsin, Madison, WI 53706 USA.
Accurate diagnosis of regional atmospheric and surface energy budgets is critical for understanding the spatial distribution of heat uptake associated with the Earth's energy imbalance (EEI). This contribution discusses frameworks and methods for consistent evaluation of key quantities of those budgets using observationally constrained data sets. It thereby touches upon assumptions made in data products which have implications for these evaluations.
View Article and Find Full Text PDFVet Rec
December 2024
School of Veterinary Medicine and Science, University of Nottingham, Sutton Bonington Campus, Loughborough, UK.
Background: Negative veterinary client complaint behaviour poses wellbeing and reputational risks. Adverse events are one source of complaint. Identifying factors that influence adverse event-related complaint behaviour is key to mitigating detrimental consequences and harnessing information that can be used to improve service quality, patient safety and business sustainability.
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