Cargando…

Network-based logistic regression integration method for biomarker identification

BACKGROUND: Many mathematical and statistical models and algorithms have been proposed to do biomarker identification in recent years. However, the biomarkers inferred from different datasets suffer a lack of reproducibilities due to the heterogeneity of the data generated from different platforms o...

Descripción completa

Detalles Bibliográficos
Autores principales:	Zhang, Ke, Geng, Wei, Zhang, Shuqin
Formato:	Online Artículo Texto
Lenguaje:	English
Publicado:	BioMed Central 2018
Materias:	Research
Acceso en línea:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6311907/ https://www.ncbi.nlm.nih.gov/pubmed/30598085 http://dx.doi.org/10.1186/s12918-018-0657-8

Ejemplares similares

Regularized logistic regression with network-based pairwise interaction for biomarker identification in breast cancer
por: Wu, Meng-Yun, et al.
Publicado: (2016)

Identification of miRNA biomarkers for breast cancer by combining ensemble regularized multinomial logistic regression and Cox regression
por: Li, Juntao, et al.
Publicado: (2022)

Multivariable logistic and linear regression models for identification of clinically useful biomarkers for osteoarthritis
por: Liem, Yulia, et al.
Publicado: (2020)

Prognostic models for breast cancer: based on logistics regression and Hybrid Bayesian Network
por: Su, Fan, et al.
Publicado: (2023)

A P300-Detection Method Based on Logistic Regression and a Convolutional Neural Network
por: Li, Qi, et al.
Publicado: (2022)

Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model
por: Zhou, Zhiming, et al.
Publicado: (2021)

A novel hybrid method of beta-turn identification in protein using binary logistic regression and neural network
por: Asghari, Mehdi Poursheikhali, et al.
Publicado: (2012)

Applied logistic regression /
por: Hosmer, David W.
Publicado: (2000)

Applied logistic regression
por: Hosmer, David W, et al.
Publicado: (2013)

Sparse logistic regression revealed the associations between HBV PreS quasispecies and hepatocellular carcinoma
por: Jia, Jian-an, et al.
Publicado: (2022)

FRL: An Integrative Feature Selection Algorithm Based on the Fisher Score, Recursive Feature Elimination, and Logistic Regression to Identify Potential Genomic Biomarkers
por: Ge, Chenyu, et al.
Publicado: (2021)

Primer on binary logistic regression
por: Harris, Jenine K
Publicado: (2021)

Logistic regression : a self-learning text /
por: Kleinbaum, David G.
Publicado: (2002)

Logistic regression : a self-learning text /
por: Kleinbaum, David G.
Publicado: (1994)

Optimizing acute stroke outcome prediction models: Comparison of generalized regression neural networks and logistic regressions
por: Qu, Sheng, et al.
Publicado: (2022)

Logistic regression analysis for the identification of the metastasis-associated signaling pathways of osteosarcoma
por: Liu, Yang, et al.
Publicado: (2018)

Identification of Grouped Rare and Common Variants via Penalized Logistic Regression
por: Ayers, Kristin L, et al.
Publicado: (2013)

Privacy-preserving logistic regression training
por: Bonte, Charlotte, et al.
Publicado: (2018)

Endogeneity in Logistic Regression Models
por: Avery, George
Publicado: (2005)

Understanding logistic regression analysis
por: Sperandei, Sandro
Publicado: (2014)

Logistic Regression—Explanation and Use
por: Hall, G H, et al.
Publicado: (1994)

Logistic Regression in Medical Research
por: Schober, Patrick, et al.
Publicado: (2021)

Practical guide to logistic regression
por: Hilbe, Joseph M
Publicado: (2016)

Purposeful selection of variables in logistic regression
por: Bursac, Zoran, et al.
Publicado: (2008)

Supporting Regularized Logistic Regression Privately and Efficiently
por: Li, Wenfa, et al.
Publicado: (2016)

Validation of in silico biomarkers for drug screening through ordinal logistic regression
por: Jeong, Da Un, et al.
Publicado: (2022)

On the consequences of model misspecification in logistic regression.
por: Begg, M D, et al.
Publicado: (1990)

Hybrid Integration Approach of Entropy with Logistic Regression and Support Vector Machine for Landslide Susceptibility Modeling
por: Zhang, Tingyu, et al.
Publicado: (2018)

Standardisation of rates using logistic regression: a comparison with the direct method
por: Roalfe, Andrea K, et al.
Publicado: (2008)

A simple method for estimating relative risk using logistic regression
por: Diaz-Quijano, Fredi A
Publicado: (2012)

An Improved Logistic Regression Method for Assessing the Performance of Track and Field Sports
por: Zheng, Songling, et al.
Publicado: (2022)

Integrated network analysis and logistic regression modeling identify stage-specific genes in Oral Squamous Cell Carcinoma
por: Randhawa, Vinay, et al.
Publicado: (2015)

Multivariable Logistic Regression And Back Propagation Artificial Neural Network To Predict Diabetic Retinopathy
por: Yao, Litong, et al.
Publicado: (2019)

Large Scale Identification and Categorization of Protein Sequences Using Structured Logistic Regression
por: Pedersen, Bjørn P., et al.
Publicado: (2014)

Gene and pathway identification with L(p )penalized Bayesian logistic regression
por: Liu, Zhenqiu, et al.
Publicado: (2008)

Logistic regression model training based on the approximate homomorphic encryption
por: Kim, Andrey, et al.
Publicado: (2018)

Identified GNGT1 and NMU as Combined Diagnosis Biomarker of Non-Small-Cell Lung Cancer Utilizing Bioinformatics and Logistic Regression
por: Zhang, Jia-Jia, et al.
Publicado: (2021)

Construction of Enterprise Financial Early Warning Model Based on Logistic Regression and BP Neural Network
por: Lyu, Jincheng
Publicado: (2022)

Privacy-preserving logistic regression with secret sharing
por: Ghavamipour, Ali Reza, et al.
Publicado: (2022)

Comparison of logistic-regression based methods for simple mediation analysis with a dichotomous outcome variable
por: Rijnhart, Judith J. M., et al.
Publicado: (2019)

Cannot write session to /tmp/vufind_sessions/sess_nvr2pfd45gmqmct2hgcqi5qpkc