» Articles » PMID: 33682765

Cancer Classification and Biomarker Selection Via a Penalized Logsum Network-based Logistic Regression Model

Overview
Publisher Sage Publications
Date 2021 Mar 8
PMID 33682765
Citations 7
Authors
Affiliations
Soon will be listed here.
Abstract

Background: In genome research, it is particularly important to identify molecular biomarkers or signaling pathways related to phenotypes. Logistic regression model is a powerful discrimination method that can offer a clear statistical explanation and obtain the classification probability of classification label information. However, it is unable to fulfill biomarker selection.

Objective: The aim of this paper is to give the model efficient gene selection capability.

Methods: In this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification.

Results: Experimental results on simulated data sets show that our method is effective in the analysis of high-dimensional data. For a large data set, the proposed method has achieved 89.66% (training) and 90.02% (testing) AUC performances, which are, on average, 5.17% (training) and 4.49% (testing) better than mainstream methods.

Conclusions: The proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.

Citing Articles

Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.

Rong R, Shen Y, Wu S, Xu S, Hu J, Zou J BMC Cancer. 2024; 24(1):1472.

PMID: 39614215 PMC: 11606021. DOI: 10.1186/s12885-024-12928-w.


Elucidating common biomarkers and pathways of osteoporosis and aortic valve calcification: insights into new therapeutic targets.

Lan Y, Peng Q, Shen J, Liu H Sci Rep. 2024; 14(1):27827.

PMID: 39537712 PMC: 11560947. DOI: 10.1038/s41598-024-78707-6.


Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.

Zou J, Shen Y, Wu S, Wei H, Li Q, Xu S Technol Cancer Res Treat. 2024; 23:15330338231219352.

PMID: 38233736 PMC: 10865948. DOI: 10.1177/15330338231219352.


Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models.

Zou C, Su L, Pan M, Chen L, Li H, Zou C Front Aging Neurosci. 2023; 15:1079433.

PMID: 36875704 PMC: 9978156. DOI: 10.3389/fnagi.2023.1079433.


Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.

He M, Liang Y, Huang H Technol Health Care. 2022; 30(S1):451-457.

PMID: 35124619 PMC: 9028654. DOI: 10.3233/THC-THC228041.


References
1.
Li Z, Chim J, Yang M, Ye J, Wong B, Qiao L . Role of PCDH10 and its hypermethylation in human gastric cancer. Biochim Biophys Acta. 2011; 1823(2):298-305. DOI: 10.1016/j.bbamcr.2011.11.011. View

2.
Friedman J, Hastie T, Tibshirani R . Regularization Paths for Generalized Linear Models via Coordinate Descent. J Stat Softw. 2010; 33(1):1-22. PMC: 2929880. View

3.
Chen J, Zhang S . Integrative analysis for identifying joint modular patterns of gene-expression and drug-response data. Bioinformatics. 2016; 32(11):1724-32. DOI: 10.1093/bioinformatics/btw059. View

4.
Kong H, Yoon S, Park J . The regulatory mechanism of the LY6K gene expression in human breast cancer cells. J Biol Chem. 2012; 287(46):38889-900. PMC: 3493930. DOI: 10.1074/jbc.M112.394270. View

5.
. Comprehensive molecular portraits of human breast tumours. Nature. 2012; 490(7418):61-70. PMC: 3465532. DOI: 10.1038/nature11412. View