Results 101 to 110 of about 1,808,699 (310)
Missing Data From Missing Participants [PDF]
Miriam J, Johnson +3 more
openaire +2 more sources
Structural and biochemical characterisations show that the planar cell polarity (PCP) protein Inturned harbours a unique PDZ‐like domain that does not bind canonical PDZ‐binding motifs (PBMs) like that of another PCP protein Vangl2. In contrast, the apical‐basal polarity protein Scribble contains four PDZ domains that bind Vangl2, but one PDZ domain ...
Stephan Wilmes +4 more
wiley +1 more source
Applying data mining algorithms to inpatient dataset with missing values
Purpose - Data preparation plays an important role in data mining as most real life data sets contained missing data. This paper aims to investigate different treatment methods for missing data.
Liu, P. +5 more
core +1 more source
Spatial effects with missing data
In recent years, there has been an increased attention and interest in quantitative and statistical models of language contact and language diffusion in space.
Naranjo Matías Guzmán +2 more
doaj +1 more source
Due to the discrepancy in resolution between existing global climate model output and the resolution required by decision-makers, there is a persistent need for climate downscaling.
Raghdah Rasyidah Abdul Rashid +6 more
doaj +1 more source
Tau acetylation at K331 has limited impact on tau pathology in vivo
We mapped tau post‐translational modifications in humanized MAPT knock‐in mice and in amyloid‐bearing double knock‐in mice. Acetylation within the repeat domain, particularly around K331, showed modest increases under amyloid pathology. To test functional relevance, we generated MAPTK331Q knock‐in mice.
Shoko Hashimoto +3 more
wiley +1 more source
Missing ordinal covariates with informative selection [PDF]
This paper considers the problem of parameter estimation in a model for a continuous response variable y when an important ordinal explanatory variable x is missing for a large proportion of the sample.
Alfonso Miranda, Sophia Rabe-Hesketh
core
Missing data is a widespread problem that can affect the ability to use data to construct effective prediction systems. We investigate a common machine learning technique that can tolerate missing values, namely C4.5, to predict cost using six real world
Liu, J, Song, Q, Shepperd, MJ, Chen, X
core +1 more source
A Label Propagation Approach for Missing Data Imputation
Missing data is a common challenge in real-world datasets and can arise for various reasons. This has led to the classification of missing data mechanisms as missing completely at random, missing at random, or missing not at random.
Filipe Loyola Lopes +5 more
doaj +1 more source

