Results 51 to 60 of about 287,343 (334)

Handling missing data in research

Perspectives in Clinical Research
Missing data are an inevitable part of research and lead to a decrease in the size of the analyzable population, and biased and imprecise estimates. In this article, we discuss the types of missing data, methods to handle missing data and suggest ways in
Priya Ranganathan, Sally Hunsberger
doaj   +1 more source

Random forest-based imputation outperforms other methods for imputing LC-MS metabolomics data: a comparative study

BMC Bioinformatics, 2019
Background LC-MS technology makes it possible to measure the relative abundance of numerous molecular features of a sample in single analysis. However, especially non-targeted metabolite profiling approaches generate vast arrays of data that are prone to
Marietta Kokla, Jyrki Virtanen, Marjukka Kolehmainen, Jussi Paananen, Kati Hanhineva   +4 more
doaj   +1 more source

A Pattern-Recognition-Based Ensemble Data Imputation Framework for Sensors from Building Energy Systems

Sensors, 2020
Building operation data are important for monitoring, analysis, modeling, and control of building energy systems. However, missing data is one of the major data quality issues, making data imputation techniques become increasingly important.
Liang Zhang
doaj   +1 more source

Calibrated imputation of numerical data under linear edit restrictions [PDF]

, 2008
A common problem faced by statistical offices is that data may be missing from collected data sets. The typical way to overcome this problem is to impute the missing data.
De Waal, Ton, Pannekoek, Jeroen, Shlomo, Natalie   +2 more
core  

Identification of serum protein biomarkers for pre‐cancerous lesions associated with pancreatic ductal adenocarcinoma

Molecular Oncology, EarlyView.
This work identified serum proteins associated with pancreatic epithelial neoplasms (PanINs) and early‐stage PDAC. Proteomics screens assessed genetically engineered mice with abundant PanINs, KPC mice (Lox‐STOP‐Lox‐KrasG12D/+ Lox‐STOP‐Lox‐Trp53R172H/+ Pdx1‐Cre) before PDAC development and also early‐stage PDAC patients (n = 31), compared to benign ...
Hannah Mearns, Jaclyn S. Long, Sergio Lilla, Kelly Hodge, Marcus J. G. W. Ladds, Colin Nixon, Paula Fernández‐Palanca, Sara Zanivan, Pilar Acedo, Stephen P. Pereira, Kevin M. Ryan   +10 more
wiley   +1 more source

Network divergence analysis identifies adaptive gene modules and two orthogonal vulnerability axes in pancreatic cancer

Molecular Oncology, EarlyView.
Tumors contain diverse cellular states whose behavior is shaped by context‐dependent gene coordination. By comparing gene–gene relationships across biological contexts, we identify adaptive transcriptional modules that reorganize into distinct vulnerability axes.
Brian Nelson, Lyanne Delgado‐Coka, Natalia Marchenko, Luisa F. Escobar‐Hoyos, Kenneth R. Shroyer, Alisa Yurovsky, Trey Ideker, Gábor Balázsi, Thomas MacCarthy, Scott Powers   +9 more
wiley   +1 more source

A Novel Missing Data Imputation Approach for Time Series Air Quality Data Based on Logistic Regression

Atmosphere, 2022
Missing values in air quality datasets bring trouble to exploration and decision making about the environment. Few imputation methods aim at time series air quality data so that they fail to handle the timeliness of the data.
Mei Chen, Hongyu Zhu, Yongxu Chen, Youshuai Wang   +3 more
doaj   +1 more source

Long‐Term Follow‐Up of Chemotherapy‐Associated Biological Aging in Women With Early Breast Cancer

Aging and Cancer, EarlyView.
Women threated with adjuvant chemotherapy for early breast cancer have sustained long‐term increase in p16INK4a,, a robust marker of cell senescence, suggesting a chemotherapy‐associated age acceleration. p16INK4a as well as other biomarkers may identify patients at greatest risk for senescence‐related diseases of aging.
Hyman B. Muss, Alexis C. Wardell, Allison M. Deal, Kirsten A. Nyrop, Allison Ross, Yara G. Abdou, Jeffrey D. Aldrich, Lisa A. Cary, E. Claire Dees, Katherine E. Reeder‐Hayes, Emily M. Ray, Natalia Mitin, Denis Tsygankov   +12 more
wiley   +1 more source

Long-Term Missing Value Imputation for Time Series Data Using Deep Neural Networks [PDF]

, 2022
Jangho Park, Juliane Müller, Bhavna Arora, Boris Faybishenko, Gilberto Pastorello, Charuleka Varadharajan, Reetik Kumar Sahu, D. Agarwal   +7 more
openalex   +1 more source

Fluid and Neuroimaging Biomarkers in Microgliopathy Colony‐Stimulating Factor‐1 Receptor‐Related Disorders

Annals of Clinical and Translational Neurology, EarlyView.
ABSTRACT Objective This study aims to identify both fluid and neuroimaging biomarkers for CSF1R‐RD that can inform the optimal timing of treatment administration to maximize therapeutic benefit, while also providing sensitive quantitative measurements to monitor disease progression.
Tomasz Chmiela, Madison Reeves, Karen Jansen‐West, Judith Dunmore, Yuping Song, Audrey Strongosky, Sunil Gandhi, Gilana Pikover, Matt Blurton‐Jones, Robert C. Spitale, Erik H. Middlebrooks, Leonard Petrucelli, Mercedes Prudencio, Zbigniew K. Wszolek   +13 more
wiley   +1 more source