Results 241 to 250 of about 23,712,988 (293)

ChASM: a statistically rigorous method for the detection of chromosomal aneuploidies in ancient DNA studies. [PDF]

Bioinformatics
Rohrlach AB, Tuke J, Prüfer K, Haak W.
europepmc   +1 more source

Meta-analysis models relaxing the random-effects normality assumption: methodological systematic review and simulation study. [PDF]

BMC Med Res Methodol
Panagiotopoulou K, Evrenoglou T, Schmid CH, Metelli S, Chaimani A.   +4 more
europepmc   +1 more source

Harnessing Latent Dirichlet Allocation-based topic modeling to unveil the focus areas in Ayush healthcare in India. [PDF]

J Ayurveda Integr Med
Dutta T, Kumar A.
europepmc   +1 more source

Multi-Level Variable Selection Using a BART-Enhanced Mixed-Effects Framework. [PDF]

Stat Med
Zhang K, Li Y, Zou J, Wang S, Fausto BA, Hu L.   +5 more
europepmc   +1 more source

Analyzing gendered patterns in sentiments in comments under STEM YouTube channels. [PDF]

Front Artif Intell
Karn I, Ilakiyaselvan N, Kalyanasundaram V, Keerthi AJ.   +3 more
europepmc   +1 more source

A New Dirichlet-Multinomial Mixture Regression Model for the Analysis of Microbiome Data. [PDF]

Stat Med
Ascari R, Migliorati S, Ongaro A.
europepmc   +1 more source

Bayesian hierarchical mixture modelling to derive probabilistic iELISA thresholds for bovine brucellosis in endemic dairy systems. [PDF]

PLoS One
Alam MS, Islam MN, Adhikari BJ, Islam S, Mahmud Hasan RS, Rahman MS, Islam MA, Aktaruzzaman M, Meletis L, Kostoulas P, Rahman AKMA.   +10 more
europepmc   +1 more source

Mitogenome-Informed Metabarcoding for Non-Invasive Identification of Sympatric Flamingos in Northern Chile. [PDF]

Biology (Basel)
Barría-Iriarte S, Soto-Jiménez D, Salcedo-Díaz F, Labra-Oróstica N, Aguilar P, Valladares-Faúndez P, Muñoz JP, Quezada-Romegialli C.   +7 more
europepmc   +1 more source

BMDD: A probabilistic framework for accurate imputation of zero-inflated microbiome sequencing data. [PDF]

PLoS Comput Biol
Zhou H, Chen J, Zhang X.
europepmc   +1 more source

GA-AFedOD: gradient-aligned active federated learning for resource-aware object detection in edge industrial IoT. [PDF]

Front Artif Intell
Wang Z, Yuan X, Chen J.
europepmc   +1 more source