Results 31 to 40 of about 179,516 (314)

Impact of Missing Data on Data Quality in Social Research

Соціологічні студії
Missing data is a common issue in quantitative social research that negatively affects the data quality. This article explores the consequences of missing data, outlining the potential issues it may pose and emphasizing the importance of properly ...
Yaroslav Kostenko
doaj   +1 more source

Context-Aware Data Imputation: Application of Domain-Agnostic Deep Imputation Network

IEEE Access
Data imputation (DI) is a crucial task to manage missing data across different domains, such as healthcare and finance. Traditional imputation methods often fail to account for contextual nuances within specific domains due to the heterogeneity of data ...
Mohammed Gh. Al Zamil, Omar Khalid Alomeir   +1 more
doaj   +1 more source

A novel scaling methodology to reduce the biases associated with missing data from commercial activity monitors.

PLoS ONE, 2020
BackgroundCommercial physical activity monitors have wide utility in the assessment of physical activity in research and clinical settings, however, the removal of devices results in missing data and has the potential to bias study conclusions.
R O'Driscoll, J Turicchi, C Duarte, J Michalowska, S C Larsen, A L Palmeira, B L Heitmann, G W Horgan, R J Stubbs   +8 more
doaj   +1 more source

Detection of circulating tumor DNA in colorectal cancer patients using a methylation‐specific droplet digital PCR multiplex

Molecular Oncology, EarlyView.
We developed a cost‐effective methylation‐specific droplet digital PCR multiplex assay containing tissue‐conserved and tumor‐specific methylation markers. The assay can detect circulating tumor DNA with high accuracy in patients with localized and metastatic colorectal cancer.
Luisa Matos do Canto, Louise Raunkilde, Jan Lindebjerg, Mads Malik Aagaard, Christina Therkildsen, Jakob Kleif, Lars Henrik Jensen, Torben Frøstrup Hansen, Rikke Fredslund Andersen   +8 more
wiley   +1 more source

Next‐generation proteomics improves lung cancer risk prediction

Molecular Oncology, EarlyView.
This is one of very few studies that used prediagnostic blood samples from participants of two large population‐based cohorts. We identified, evaluated, and validated an innovative protein marker model that outperformed an established risk prediction model and criteria employed by low‐dose computed tomography in lung cancer screening trials.
Megha Bhardwaj, Clara Frick, Ben Schöttker, Bernd Holleczek, Hermann Brenner   +4 more
wiley   +1 more source

Missing Categorical Data in Sociological Surveys: An Experimental Evaluation of Imputation Techniques

Соціологічні студії
Missing categorical data presents a persistent challenge to data quality in quantitative sociological research, where simpler approaches can lead to biased estimates and incorrect conclusions.
Yaroslav Kostenko, Andrii Gorbachyk
doaj   +1 more source

MIAEC: Missing Data Imputation Based on the Evidence Chain

IEEE Access, 2018
Missing or incorrect data caused by improper operations can seriously compromise security investigation. Missing data can not only damage the integrity of the information but also lead to the deviation of the data mining and analysis.
Xiaolong Xu, Weizhi Chong, Shancang Li, Abdullahi Arabo, Jianyu Xiao   +4 more
doaj   +1 more source

Effects of Different Missing Data Imputation Techniques on the Performance of Undiagnosed Diabetes Risk Prediction Models in a Mixed-Ancestry Population of South Africa. [PDF]

PLoS ONE, 2015
Imputation techniques used to handle missing data are based on the principle of replacement. It is widely advocated that multiple imputation is superior to other imputation methods, however studies have suggested that simple methods for filling missing ...
Katya L Masconi, Tandi E Matsha, Rajiv T Erasmus, Andre P Kengne   +3 more
doaj   +1 more source

Cell surface interactome analysis identifies TSPAN4 as a negative regulator of PD‐L1 in melanoma

Molecular Oncology, EarlyView.
Using cell surface proximity biotinylation, we identified tetraspanin TSPAN4 within the PD‐L1 interactome of melanoma cells. TSPAN4 negatively regulates PD‐L1 expression and lateral mobility by limiting its interaction with CMTM6 and promoting PD‐L1 degradation.
Guus A. Franken, Andrea Abel Gutierrez, Imke van Rossum, Cornelia G. Spruijt, Michiel Vermeulen, Guido van Mierlo, Blanca Scheijen, Annemiek B. van Spriel   +7 more
wiley   +1 more source

Generative Adversarial Networks Assist Missing Data Imputation: A Comprehensive Survey and Evaluation

IEEE Access, 2023
Missing data imputation is a technique to deal with incomplete datasets. Since many models and algorithms cannot be applied to data containing missing values, a pre-processing step needs to be performed to remove incomplete data or to estimate the ...
Reza Shahbazian, Sergio Greco
doaj   +1 more source