ArticleTechnology and health care : official journal of the European Society for Engineering and Medicine2021
Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model.
Article in Technology and health care : official journal of the European Society for Engineering and Medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- Article
- Clinical prediction of pathological complete response in breast cancer: a machine learning study.BMC cancer · 2025Article
- Prediction model for ocular metastasis of breast cancer: machine learning model development and interpretation study.BMC cancer · 2024Article
- Elucidating common biomarkers and pathways of osteoporosis and aortic valve calcification: insights into new therapeutic targets.Scientific reports · 2024Article
- Exploration of novel biomarkers in Alzheimer's disease based on four diagnostic models.Frontiers in aging neuroscience · 2023Article
- Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD).Biochemical Society transactions · 2022Article
- Integrating molecular interactions and gene expression to identify biomarkers to predict response to tumor necrosis factor inhibitor therapies in rheumatoid arthritis patients.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Integrating molecular interactions and gene expression to identify biomarkers and network modules of chronic obstructive pulmonary disease.Technology and health care : official journal of the European Society for Engineering and Medicine · 2022Article
- Prediction Model of Ocular Metastases in Gastric Adenocarcinoma: Machine Learning-Based Development and Interpretation Study.Technology in cancer research & treatmentArticle
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Authors and funding
3 authors.
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Abstract
backgroundIn 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.
objectiveThe aim of this paper is to give the model efficient gene selection capability.
methodsIn this paper, we propose a new penalized logsum network-based regularization logistic regression model for gene selection and cancer classification.
resultsExperimental 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.
conclusionsThe proposed method can be considered a promising tool for gene selection and cancer classification of high-dimensional biological data.
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