Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

Zhou, Zhiming; Huang, Haihui; Liang, Yong

doi:10.3233/thc-218026

Cited by 8 publications

(5 citation statements)

References 29 publications

(48 reference statements)

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“…Furthermore, logistic regression is a powerful discriminative method that clearly explains statistics and can also derive relevant classification probabilities ( Zhou et al, 2021 ). Some studies have reported that logistic regression can assess the strongest association with outcome among various factors, which can be “adjust” for other predictor variables and factors related to outcome, without being affected by confounding factors ( Tolles and Meurer, 2016 ).…”

Section: Methodsmentioning

confidence: 99%

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

Zou

Pan

et al. 2023

Front. Aging Neurosci.

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BackgroundDespite tremendous progress in diagnosis and prediction of Alzheimer’s disease (AD), the absence of treatments implies the need for further research. In this study, we screened AD biomarkers by comparing expression profiles of AD and control tissue samples and used various models to identify potential biomarkers. We further explored immune cells associated with these biomarkers that are involved in the brain microenvironment.MethodsBy differential expression analysis, we identified differentially expressed genes (DEGs) of four datasets (GSE125583, GSE118553, GSE5281, GSE122063), and common expression direction of genes of four datasets were considered as intersecting DEGs, which were used to perform enrichment analysis. We then screened the intersecting pathways between the pathways identified by enrichment analysis. DEGs in intersecting pathways that had an area under the curve (AUC) > 0.7 constructed random forest, least absolute shrinkage and selection operator (LASSO), logistic regression, and gradient boosting machine models. Subsequently, using receiver operating characteristic curve (ROC) and decision curve analysis (DCA) to select an optimal diagnostic model, we obtained the feature genes. Feature genes that were regulated by differentially expressed miRNAs (AUC > 0.85) were explored further. Furthermore, using single-sample GSEA to calculate infiltration of immune cells in AD patients.ResultsScreened 1855 intersecting DEGs that were involved in RAS and AMPK signaling. The LASSO model performed best among the four models. Thus, it was used as the optimal diagnostic model for ROC and DCA analyses. This obtained eight feature genes, including ATP2B3, BDNF, DVL2, ITGA10, SLC6A12, SMAD4, SST, and TPI1. SLC6A12 is regulated by miR-3176. Finally, the results of ssGSEA indicated dendritic cells and plasmacytoid dendritic cells were highly infiltrated in AD patients.ConclusionThe LASSO model is the optimal diagnostic model for identifying feature genes as potential AD biomarkers, which can supply new strategies for the treatment of patients with AD.

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Section: Methodsmentioning

confidence: 99%

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

Zou

Pan

et al. 2023

Front. Aging Neurosci.

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“…All algorithm models were built using scikit-learn (Version 0.24.2). In this research, we utilized six ML algorithms: multilayer perceptron (MLP) 18 ; adaboost (AB) model 19 ; bagging classification (BAG) model 20 ; logistic regression (LR) 21 ; gradient boosting machine (GBM) 22 ; and extreme gradient boosting (XGB) model. 23 The ML algorithm was trained and modulated to forecast OM in patients with GA.…”

Section: Methodsmentioning

confidence: 99%

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

Zou,

Shen,

et al. 2024

Technol Cancer Res Treat

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Background: Although gastric adenocarcinoma (GA) related ocular metastasis (OM) is rare, its occurrence indicates a more severe disease. We aimed to utilize machine learning (ML) to analyze the risk factors of GA-related OM and predict its risks. Methods: This is a retrospective cohort study. The clinical data of 3532 GA patients were collected and randomly classified into training and validation sets in a ratio of 7:3. Those with or without OM were classified into OM and non-OM (NOM) groups. Univariate and multivariate logistic regression analyses and least absolute shrinkage and selection operator were conducted. We integrated the variables identified through feature importance ranking and further refined the selection process using forward sequential feature selection based on random forest (RF) algorithm before incorporating them into the ML model. We applied six ML algorithms to construct the predictive GA model. The area under the receiver operating characteristic (ROC) curve indicated the model's predictive ability. Also, we established a network risk calculator based on the best performance model. We used Shapley additive interpretation (SHAP) to identify risk factors and to confirm the interpretability of the black box model. We have de-identified all patient details. Results: The ML model, consisting of 13 variables, achieved an optimal predictive performance using the gradient boosting machine (GBM) model, with an impressive area under the curve (AUC) of 0.997 in the test set. Utilizing the SHAP method, we identified crucial factors for OM in GA patients, including LDL, CA724, CEA, AFP, CA125, Hb, CA153, and Ca2+. Additionally, we validated the model's reliability through an analysis of two patient cases and developed a functional online web prediction calculator based on the GBM model. Conclusion: We used the ML method to establish a risk prediction model for GA-related OM and showed that GBM performed best among the six ML models. The model may identify patients with GA-related OM to provide early and timely treatment.

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“…Penalized Logistic Regression evaluates significant interactions between features in an n-by-p data matrix and the categorical outcome [28]. It can be used to efficiently identify the most influential descriptors to build a QSAR classification model with both high prediction accuracy and easy interpretability.…”

Section: Classification Penalized Logistic Regressionmentioning

confidence: 99%

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

Lee

Maria-Solano

et al. 2022

Biochemical Society Transactions

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There have been numerous advances in the development of computational and statistical methods and applications of big data and artificial intelligence (AI) techniques for computer-aided drug design (CADD). Drug design is a costly and laborious process considering the biological complexity of diseases. To effectively and efficiently design and develop a new drug, CADD can be used to apply cutting-edge techniques to various limitations in the drug design field. Data pre-processing approaches, which clean the raw data for consistent and reproducible applications of big data and AI methods are introduced. We include the current status of the applicability of big data and AI methods to drug design areas such as the identification of binding sites in target proteins, structure-based virtual screening (SBVS), and absorption, distribution, metabolism, excretion and toxicity (ADMET) property prediction. Data pre-processing and applications of big data and AI methods enable the accurate and comprehensive analysis of massive biomedical data and the development of predictive models in the field of drug design. Understanding and analyzing biological, chemical, or pharmaceutical architectures of biomedical entities related to drug design will provide beneficial information in the biomedical big data era.

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Cancer classification and biomarker selection via a penalized logsum network-based logistic regression model

Cited by 8 publications

References 29 publications

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

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

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

Big data and artificial intelligence (AI) methodologies for computer-aided drug design (CADD)

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