Results 211 to 220 of about 37,129 (261)

Validating Physiologically‐Based Pharmacokinetic Models Using the Continuous Ranked Probability Score: Beyond Being Correct on Average

CPT: Pharmacometrics &Systems Pharmacology, Volume 15, Issue 2, February 2026.
ABSTRACT Physiologically‐based pharmacokinetic (PBPK) models have become increasingly popular for model‐informed drug development (MIDD) over the past decade. While several guidelines for model evaluation exist, these are by design often of a general and non‐specific nature. It is clear what steps should be carried out but not necessarily how.
Laurens Sluijterman, Marjolein van Borselen, Rick Greupink, Joanna IntHout   +3 more
wiley   +1 more source

Time-Series Autoregressive Models for Point and Interval Forecasting of Raw and Derived Commercial Near-Infrared Spectroscopy Measures: An Exploratory Cranial Trauma and Healthy Control Analysis. [PDF]

Bioengineering (Basel)
Sainbhi AS, Froese L, Stein KY, Vakitbilir N, Hasan R, Gomez A, Bergmann T, Silvaggio N, Hayat M, Moon J, Zeiler FA.   +10 more
europepmc   +1 more source

Robust Confidence Intervals for Process Capability Index Based on Cost and Loss Functions With Bootstrap Enhancement

Quality and Reliability Engineering International, Volume 42, Issue 1, Page 212-224, February 2026.
ABSTRACT Process capability indices (PCIs) are commonly used to evaluate a process's performance in relation to the standards. Greater PCIs do not necessarily translate into lower rejection rates, even though they do indicate a higher level of process quality. Thus, a loss‐ and cost‐based PCI is a better way to measure process capability.
Sanku Dey, Chanseok Park
wiley   +1 more source

Robust Distribution‐Free Tests for the Linear Model

Statistics in Medicine, Volume 45, Issue 3-5, February 2026.
ABSTRACT Recently, there has been growing concern about heavy‐tailed and skewed noise in biological data. We introduce RobustPALMRT, a flexible permutation framework for testing the association of a covariate of interest adjusted for control covariates.
Torey Hilbert, Steven N. MacEachern, Yuan Zhang   +2 more
wiley   +1 more source

Benchmarking interpretability of deep learning for predictive genomics: Recall, precision, and variability of feature attribution. [PDF]

PLoS Comput Biol
Reynolds J, Pan C.
europepmc   +1 more source

Violence, Volition, and Volatility: The Embodied Subjectivity of Women in Cults

Symbolic Interaction, Volume 49, Issue 1, Page 113-133, February 2026.
This paper explores the embodied experience of 25 women who are former cult members. By delving into the stories of three protagonists, we examine how these women engaged with and possibly redefined the cult's socially constructed notion of womanhood.
Shirly Bar‐Lev, Michal Morag
wiley   +1 more source

Objective perimetry and diabetic retinopathy progression: a 10-year follow-up study. [PDF]

Front Endocrinol (Lausanne)
Yibekal BT, Rai BB, van Kleef JP, Sabeti F, Rohan EMF, Nolan CJ, Maddess T.   +6 more
europepmc   +1 more source

Darkened Snow Triggers Different Snowmelt Responses Over Contrasting Water Years in Great Salt Lake Headwater Basins

Water Resources Research, Volume 62, Issue 2, February 2026.
Abstract Snow in the Great Salt Lake Basin is a vital resource for regional agriculture, municipal water use, and the Great Salt Lake. Accumulation of light absorbing particles (LAPs) on mountain snowpacks results in lower albedos and earlier melt compared to clean snow.
Otto I. Lang, Joachim Meyer, J. Michelle Hu, S. McKenzie Skiles   +3 more
wiley   +1 more source

Methylation reference datasets from quartet DNA materials for benchmarking epigenome sequencing. [PDF]

Nat Commun
Guo X, Chen Q, Zhang Y, Zhang Y, Liu Y, Duan S, Ma Y, Ni P, Wang J, He B, Ren L, Ma R, Hou W, Yu Y, Li B, Qiu F, Sun Y, Zhang Z, Xu W, Fang X, Li J, Shi L, Zhang R, Zheng Y, Dong L.   +24 more
europepmc   +1 more source

Cooperative learning of Pl@ntNet's Artificial Intelligence algorithm: How does it work and how can we improve it?

Methods in Ecology and Evolution, Volume 17, Issue 2, Page 392-403, February 2026.
Abstract Deep learning models for plant species identification rely on large annotated datasets. The Pl@ntNet system enables global data collection by allowing users to upload and annotate plant observations, leading to noisy labels due to diverse user skills.
Tanguy Lefort, Antoine Affouard, Benjamin Charlier, Jean‐Christophe Lombardo, Mathias Chouet, Hervé Goëau, Joseph Salmon, Pierre Bonnet, Alexis Joly   +8 more
wiley   +1 more source