Results 51 to 60 of about 160,067 (256)
Molecular Oncology, EarlyView.Single‐cell multi‐omics reveals epigenetic heterogeneity across therapy‐adaptive tumor states, including quiescent/dormant, drug‐tolerant persister, and EMT‐like phenotypes. By linking regulatory features with state‐associated biomarkers, these approaches inform biomarker‐guided therapeutic strategies for evolving tumors.
Hee Jung Kim +3 more
wiley +1 more source
Missing Value Imputation for RNA-Sequencing Data Using Statistical Models: A Comparative Study [PDF]
Journal of Statistical Theory and Applications (JSTA), 2016 RNA-seq technology has been widely used as an alternative approach to traditional microarrays in transcript analysis. Sometimes gene expression by sequencing, which generates RNA-seq data set, may have missing read counts.
Taban Baghfalaki +2 more
doaj +1 more source
Molecular Oncology, EarlyView.Single‐cell DNA methylation (scDNAme) profiling maps epimutational clonal evolution, revealing mechanisms of malignancy and therapeutic resistance across diverse cancer types. By providing a high‐resolution landscape of intratumoral heterogeneity, these technologies empower precise patient stratification, guide the development of enhanced ...
Ik Soo Kim
wiley +1 more source
An Intelligent Missing Data Imputation Techniques: A Review
JOIV: International Journal on Informatics Visualization, 2022 The incomplete dataset is an unescapable problem in data preprocessing that primarily machine learning algorithms could not employ to train the model.
Kimseth Seu, Mi-Sun Kang, HwaMin Lee
doaj +1 more source
Five‐Year Disease Progression in Synuclein Seeding Positive Sporadic Parkinson's Disease
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective To provide a comprehensive description of disease progression in synuclein seeding assay (SAA) positive sporadic Parkinson Disease participants, using Neuronal Synuclein Disease integrated biological and functional impairment staging framework.
Paulina Gonzalez‐Latapi +19 more
wiley +1 more source
A Semiparametric Method of Multiple Imputation
Journal of the Royal Statistical Society Series B: Statistical Methodology, 1998 Summary In this paper, we describe how to use multiple imputation semiparametrically to obtain estimates of parameters and their standard errors when some individuals have missing data. The methods given require the investigator to know or be able to estimate the process generating the missing data but requires no full distributional ...
Lipsitz, Stuart R. +2 more
openaire +2 more sources
Multiple imputation: dealing with missing data [PDF]
Nephrology Dialysis Transplantation, 2013 In many fields, including the field of nephrology, missing data are unfortunately an unavoidable problem in clinical/epidemiological research. The most common methods for dealing with missing data are complete case analysis-excluding patients with missing data--mean substitution--replacing missing values of a variable with the average of known values ...
Goeij, M.C.M. de +5 more
openaire +7 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective In multiple sclerosis, the optimal time for deploying a therapeutic intervention is before the central nervous system is damaged; given the success of trials treating the earliest stage of MS, the radiologically isolated syndrome, developing primary prevention strategies is an important next challenge.
Amy W. Laitinen +7 more
wiley +1 more source
Asymptotic Results for Multiple Imputation
The Annals of Statistics, 1988 For analyzing incomplete data in surveys, \textit{D. B. Rubin} [ibid. 6, 34- 58 (1978; Zbl 0383.62021)] proposed multiple-imputation where the missing data are replaced by two or more values representing a distribution of the possible values. These studies assume that the outcome variable Y is a scalar, sample selection is by simple random sampling ...
Schenker, Nathaniel, Welsh, A. H.
openaire +3 more sources
Annals of Clinical and Translational Neurology, EarlyView.ABSTRACT Objective Onasemnogene abeparvovec (OA) is an AAV9‐based gene therapy for spinal muscular atrophy type I (SMA I). Real‐world outcomes show increased response variability compared to clinical trials, and follow‐up data beyond 12–18 months are limited.
Marika Pane +43 more
wiley +1 more source