Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network

Quan, Dichen; Ren, Jiahui; Ren, Hao; Linghu, Liqin; Wang, Xuchun; Li, Meichen; Qiao, Yuchao; Ren, Zeping; Qiu, Lixia

doi:10.1038/s41598-022-11125-8

Cited by 9 publications

(6 citation statements)

References 22 publications

(23 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To predict CT-quantified emphysema, we constructed supervised elastic net models. Elastic net offers several well-known benefits, including the ability to account for multi-collinear features and avoid overfitting (42). The outcome variable was the adjusted Perc15 density.…”

Section: Methodsmentioning

confidence: 99%

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Suryadevara

Gregory

et al. 2022

Preprint

View full text Add to dashboard Cite

Rationale: Emphysema is a key component of COPD with important prognostic implications. Identifying blood-based biomarkers of emphysema will facilitate early diagnosis and possible development of targeted therapies. Objectives: Discover blood transcriptomic and proteomic biomarkers for chest computed tomography-quantified emphysema in smokers and develop predictive biomarker panels. Methods: Emphysema blood biomarker discovery was performed using differential gene expression, alternative splicing, and protein association analyses in a training set of 2,370 COPDGene participants with available whole blood RNA sequencing, plasma SomaScan proteomics, and clinical data. Validation was conducted in a testing set of 1,016 COPDGene subjects. Since body mass index (BMI) and emphysema often co-occur, we performed a mediation analysis to quantify the effect of BMI on gene and protein associations with emphysema. Predictive models were also developed using elastic net to predict quantitative emphysema from cell blood count, RNA sequencing, and proteomic biomarkers. Model accuracy was assessed by area under the receiver-operator-characteristic-curves (AUROC) for subjects stratified into tertiles of emphysema severity. Measurements and Main Results: 4,913 genes, 1,478 isoforms, 386 exons, and 881 proteins were significantly associated with emphysema (FDR 10%). 75% and 77% of genes and proteins, respectively, were mediated by BMI. The significantly enriched biological pathways were involved in inflammation and cell differentiation, differing between the most and least BMI-mediated genes. The cell blood count plus protein model achieved the highest performance with an AUROC of 0.89. Conclusions: Blood transcriptome and proteome-wide analyses reveal key biological pathways of emphysema and enhance the prediction of emphysema.

show abstract

Section: Methodsmentioning

confidence: 99%

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Suryadevara

Gregory

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

“…Finally, SCAD identi ed 14 features that are closely related to COPD. Previous research has con rmed that the variables identi ed in this study, such as frequent coughing before the age of 14, hospitalization for pneumonia or bronchitis between the ages of 15 and 17, respiratory diseases, gastroesophageal re ux, family history, current smoking, and the remaining eight characteristics, are all signi cant risk factors for COPD [30][31][32][33][34] . Given these factors, the selection of variables in this study is justi ed as reasonable.…”

Section: Discussionmentioning

confidence: 59%

Performance Comparison of Improved Machine Learning Algorithms Based on Bayesian Optimization in High-dimensional and Unbalanced COPD Data

Wang

Qiao

et al. 2023

Preprint

Self Cite

View full text Add to dashboard Cite

Background and objective: Early identification of individuals at high risk of chronic obstructive pulmonary disease (COPD) is crucial for reducing related mortality rates and economic burden. However, conventional machine learning (ML) models have limitations when making predictions using COPD data that exhibit high-dimensional and unbalanced characteristics. Therefore, to address this issue, this study developed a well-performing Bayesian optimization (BO)-ML hybrid model combined with variable screening and resampling technology to construct a COPD risk prediction model. Methods: We collected a sample of 4,747 COPD cases with no missing data from the 2019 COPD Surveillance project in Shanxi Province, and extracted 34 potentially relevant variables from the dataset. Firstly, we used the Smoothly Clipped Absolute Deviation (SCAD) method to select variables associated with COPD. Secondly, we oversampling the unbalanced data using Synthetic Minority Over-sampling Technique (SMOTE) algorithm. Thirdly, we construct risk prediction models in the training set using four BO-improved ML models, including BO-Decision Tree (DT), BO-Naive Bayes (NB), BO-Support Vector Machine (SVM) and BO-K-nearest neighbor (KNN). Finally, the predictive performance of the combined models is tested and evaluated. Result: The SCAD method was used to select 14 variables specifically associated with COPD from a dataset of 34 features. After applying the SMOTE resampling method, the ratio of COPD patients to non-COPD patients in the dataset of this study was balanced at 1:1. In the construction process of the four ML models, this study utilized BO algorithm to identify their optimal hyperparameters. Furthermore, in the comparison of model performance, this study found that combining BO-ML hybrid models with data balancing techniques can improve their performance. Specifically, the combination of SMOTE and BO-NB demonstrated stable performance and attained high scores in the comprehensive evaluation index, with AUC and G-means values of 0.770 and 0.696 respectively. Conclusion: Despite the challenges posed by high dimensionality, redundancy, and class imbalance in data set, the BO-NB model, when integrated with SCAD and SMOTE, has exhibited excellent performance in accurately identifying individuals at a high risk of COPD. It provides early warnings to clinical doctors, helping them take timely preventive measures.

show abstract

“…However, it cannot provide a comprehensive overview of the overall association between risk factors ( 15 ), nor can it detect direct or indirect risk factors. In contrast, BNs offer several advantages over logistic regression in establishing risk factor models ( 23 ). Firstly, BNs do not require any prior assumptions.…”

Section: Discussionmentioning

confidence: 99%

Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study

Zhang,

Yao

et al. 2023

Front. Public Health

View full text Add to dashboard Cite

ObjectivesWhile Bayesian networks (BNs) represents a good approach to discussing factors related to many diseases, little attention has been poured into heart attack combined with hypertension (HAH) using BNs. This study aimed to explore the complex network relationships between HAH and its related factors, and to achieve the Bayesian reasoning for HAH, thereby, offering a scientific reference for the prevention and treatment of HAH.MethodsThe data was downloaded from the Online Open Database of CHARLS 2018, a population-based longitudinal survey. In this study, we included 16 variables from data on demographic background, health status and functioning, and lifestyle. First, Elastic Net was first used to make a feature selection for highly-related variables for HAH, which were then included into BN model construction. The structural learning of BNs was achieved using Tabu algorithm and the parameter learning was conducted using maximum likelihood estimation.ResultsAmong 19,752 individuals (9,313 men and 10,439 women) aged 64.73 ± 10.32 years, Among 19,752 individuals (9,313 men and 10,439 women), there are 8,370 ones without HAH (42.4%) and 11,382 ones with HAH (57.6%). What’s more, after feature selection using Elastic Net, Physical activity, Residence, Internet access, Asset, Marital status, Sleep duration, Social activity, Educational levels, Alcohol consumption, Nap, BADL, IADL, Self report on health, and age were included into BN model establishment. BNs were constructed with 15 nodes and 25 directed edges. The results showed that age, sleep duration, physical activity and self-report on health are directly associated with HAH. Besides, educational levels and IADL could indirectly connect to HAH through physical activity; IADL and BADL could indirectly connect to HAH through Self report on health.ConclusionBNs could graphically reveal the complex network relationship between HAH and its related factors. Besides, BNs allows for risk reasoning for HAH through Bayesian reasoning, which is more consistent with clinical practice and thus holds some application prospects.

show abstract

Exploring influencing factors of chronic obstructive pulmonary disease based on elastic net and Bayesian network

Cited by 9 publications

References 22 publications

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Blood-Based Transcriptomic and Proteomic Biomarkers of Emphysema

Performance Comparison of Improved Machine Learning Algorithms Based on Bayesian Optimization in High-dimensional and Unbalanced COPD Data

Exploring factors related to heart attack complicated with hypertension using a Bayesian network model: a study based on the China Health and Retirement Longitudinal Study

Contact Info

Product

Resources

About