Cargando…

Analysis of Multivariate Disease Classification Data in the Presence of Partially Missing Disease Traits

In modern cancer epidemiology, diseases are classified based on pathologic and molecular traits, and different combinations of these traits give rise to many disease subtypes. The effect of predictor variables can be measured by fitting a polytomous logistic model to such data. The differences (hete...

Descripción completa

Detalles Bibliográficos
Autores principales:	Miao, Jingang, Sinha, Samiran, Wang, Suojin, Diver, W Ryan, Gapstur, Susan M
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	2014
Materias:	Article
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4270282/ https://www.ncbi.nlm.nih.gov/pubmed/25530913 http://dx.doi.org/10.4172/2155-6180.1000197

Ejemplares similares

Mood Disorder Detection in Adolescents by Classification Trees, Random Forests and XGBoost in Presence of Missing Data
por: Turska, Elzbieta, et al.
Publicado: (2021)

On multivariate imputation and forecasting of decadal wind speed missing data
por: Wesonga, Ronald
Publicado: (2015)

Influence of Missing Values Substitutes on Multivariate Analysis of Metabolomics Data
por: Gromski, Piotr S., et al.
Publicado: (2014)

Classification and Multivariate Analysis for Complex Data Structures
por: Fichet, Bernard, et al.
Publicado: (2011)

Estimating correlation between multivariate longitudinal data in the presence of heterogeneity
por: Gao, Feng, et al.
Publicado: (2017)

Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health
por: Pan, Steven, et al.
Publicado: (2023)

Causal Information Approach to Partial Conditioning in Multivariate Data Sets
por: Marinazzo, D., et al.
Publicado: (2012)

Viral Genetic Linkage Analysis in the Presence of Missing Data
por: Liu, Shelley H., et al.
Publicado: (2015)

Multivariate partial linear varying coefficients model for gene‐environment interactions with multiple longitudinal traits
por: Wang, Honglang, et al.
Publicado: (2022)

Classification of Alzheimer's disease based on hippocampal multivariate morphometry statistics
por: Zheng, Weimin, et al.
Publicado: (2023)

An Ensemble Method for Missing Data of Environmental Sensor Considering Univariate and Multivariate Characteristics
por: Choi, Chanyoung, et al.
Publicado: (2021)

Multimodal Classification of Parkinson’s Disease in Home Environments with Resiliency to Missing Modalities
por: Heidarivincheh, Farnoosh, et al.
Publicado: (2021)

Multi-Source Ensemble Learning for the Remote Prediction of Parkinson's Disease in the Presence of Source-Wise Missing Data
Publicado: (2018)

Efficiency of multiple imputation to test for association in the presence of missing data
por: Croiseau, Pascal, et al.
Publicado: (2007)

Multi-omics regulatory network inference in the presence of missing data
por: Henao, Juan D, et al.
Publicado: (2023)

Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability Function
por: Uechi, Lisa, et al.
Publicado: (2019)

Ischemic Heart Disease Mortality and Long-Term Exposure to Source-Related Components of U.S. Fine Particle Air Pollution
por: Thurston, George D., et al.
Publicado: (2015)

Multiple Imputation for General Missing Data Patterns in the Presence of High-dimensional Data
por: Deng, Yi, et al.
Publicado: (2016)

Computing within-study covariances, data visualization, and missing data solutions for multivariate meta-analysis with metavcov
por: Lu, Min
Publicado: (2023)

Investigating the Impact of the Missing Significant Objects in Scene Recognition Using Multivariate Pattern Analysis
por: Gu, Jin, et al.
Publicado: (2020)

Finding invisible quantitative trait loci with missing data
por: Gabur, Iulian, et al.
Publicado: (2018)

Measurement data from bobbins of Partially Oriented Yarns: Univariate and multivariate aspects
por: Almeida, Fabrício A., et al.
Publicado: (2019)

An Efficient and Effective Model to Handle Missing Data in Classification
por: Mehrabani-Zeinabad, Kamran, et al.
Publicado: (2020)

Haplotype frequency estimation error analysis in the presence of missing genotype data
por: Kelly, Enda D, et al.
Publicado: (2004)

Classification of multivariate data with a spiking neural network on neuromorphic hardware
por: Schmuker, Michael, et al.
Publicado: (2013)

Multivariate Determination of the Missing Energy in the Transverse Plane ($E^{miss}_T$) at $\sqrt{s}= 13 \TeV$
por: CMS Collaboration
Publicado: (2015)

Recurrent Neural Networks for Multivariate Time Series with Missing Values
por: Che, Zhengping, et al.
Publicado: (2018)

StatPattern Recognition for multivariate classification
por: Ilya Narsky
Publicado: (2007)

Ambient Air Pollution and Cancer Mortality in the Cancer Prevention Study II
por: Turner, Michelle C., et al.
Publicado: (2017)

Generalized empirical likelihood methods for analyzing longitudinal data
por: Wang, Suojin, et al.
Publicado: (2010)

A hierarchical Bayesian approach for handling missing classification data
por: Ketz, Alison C., et al.
Publicado: (2019)

Testing Simulation Theory with Cross-Modal Multivariate Classification of fMRI Data
por: Etzel, Joset A., et al.
Publicado: (2008)

A Kernel-Based Multivariate Feature Selection Method for Microarray Data Classification
por: Sun, Shiquan, et al.
Publicado: (2014)

Real-Time Classification of Multivariate Olfaction Data Using Spiking Neural Networks
por: Vanarse, Anup, et al.
Publicado: (2019)

Multivariate Chemometrics with Regression and Classification Analyses in Heroin Profiling Based on the Chromatographic Data
por: B. Gadžurić, Slobodan, et al.
Publicado: (2016)

Evaluation of a Novel Ambient Light Survey Question in the Cancer Prevention Study-3
por: Diver, W. Ryan, et al.
Publicado: (2023)

Estimating Measurement Error of the Patient Activation Measure for Respondents with Partially Missing Data
por: Linden, Ariel
Publicado: (2015)

TiMEG: an integrative statistical method for partially missing multi-omics data
por: Das, Sarmistha, et al.
Publicado: (2021)

A multivariable approach for risk markers from pooled molecular data with only partial overlap
por: Stelzer, Anne-Sophie, et al.
Publicado: (2019)

Deep learning for clustering of multivariate clinical patient trajectories with missing values
por: de Jong, Johann, et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_plovmn1hom4u0c3eikc1gn213c