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

Overview

Journal Technol Health Care

Publisher Sage Publications

Specialties Biomedical Engineering
Biotechnology
Health Services

Date 2021 Mar 8

PMID 33682765

Citations 7

Authors

Zhiming Zhou

Haihui Huang

Yong Liang

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

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

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

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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.

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