Spike‐and‐slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high‐dimensional data analysis

Guo, Boyi; Jaeger, Byron C.; Rahman, A K M Fazlur; Long, D. Leann; Yi, Nengjun

doi:10.1002/sim.9483

Cited by 3 publications

(1 citation statement)

References 47 publications

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“…The "caret" package ( https://github.com/topepo/caret/ ) was used to divide patients with GC (1:1) into training and test groups to construct and verify models, respectively. The packages "glmnet", "survminer" and "timeROC" were then used for the least absolute shrinkage and selection operator (LASSO), and the multivariate COX regression analysis was used for prognostic associated DEGs 34 , 35 . The risk scores (RS) of genes with non-zero regression coefficients were calculated.…”

Section: Methodsmentioning

confidence: 99%

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

Cao,

Liu,

Cheng

et al. 2023

Sci Rep

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The incidence and mortality rate of gastric cancer (GC) have remained high worldwide. Although some progress has been made in immunotargeted therapy, the treatment effect remains limited. With more attention has been paid to the immune potential of tumor-associated macrophages (TAMs), but the specific mechanisms of tumor immunity are still unclear. Thus, we screened marker genes in TAMs differentiation (MDMs) through single-cell RNA sequencing, and combined with GC transcriptome data from TCGA and GEO databases, the clinical and TME characteristics, prognostic differences, immune infiltration, and drug sensitivity among different subtypes of patients with GC in different data sets were analyzed. A prognostic model of GC was constructed to evaluate the prognosis and immunotherapy response of patients with GC. In this study, we extensively studied the mutations in MDMs such as CGN, S100A6, and C1QA, and found differences in the infiltration of immune cells and immune checkpoints including M2 TAMs, T cells, CD274, and CTLA4 in different GC subtypes. In the model, we constructed a predictive scoring system with high accuracy and screened out key MDMs-related genes associated with prognosis and M2 TAMs, among which VKORC1 may be involved in GC progression and iron death in tumor cells. Therefore, this study explores the therapeutic strategy of TAMs reprogramming in-depth, providing new ideas for the clinical diagnosis, treatment, and prognosis assessment of GC.

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Section: Methodsmentioning

confidence: 99%

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

Cao,

Liu,

Cheng

et al. 2023

Sci Rep

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Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning

Chen,

Li,

Zhou

et al. 2024

Sci Rep

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To construct a prediction model of olfactory dysfunction after transnasal sellar pituitary tumor resection based on machine learning algorithms. A cross-sectional study was conducted. From January to December 2022, 158 patients underwent transnasal sellar pituitary tumor resection in three tertiary hospitals in Sichuan Province were selected as the research objects. The olfactory status was evaluated one week after surgery. They were randomly divided into a training set and a test set according to the ratio of 8:2. The training set was used to construct the prediction model, and the test set was used to evaluate the effect of the model. Based on different machine learning algorithms, BP neural network, logistic regression, decision tree, support vector machine, random forest, LightGBM, XGBoost, and AdaBoost were established to construct olfactory dysfunction risk prediction models. The accuracy, precision, recall, F1 score, and area under the ROC curve (AUC) were used to evaluate the model's prediction performance, the optimal prediction model algorithm was selected, and the model was verified in the test set of patients. Of the 158 patients, 116 (73.42%) had postoperative olfactory dysfunction. After missing value processing and feature screening, an essential order of influencing factors of olfactory dysfunction was obtained. Among them, the duration of operation, gender, type of pituitary tumor, pituitary tumor apoplexy, nasal adhesion, age, cerebrospinal fluid leakage, blood scar formation, and smoking history became the risk factors of olfactory dysfunction, which were the key indicators of the construction of the model. Among them, the random forest model had the highest AUC of 0.846, and the accuracy, precision, recall, and F1 score were 0.750, 0.870, 0.947, and 0.833, respectively. Compared with the BP neural network, logistic regression, decision tree, support vector machine, LightGBM, XGBoost, and AdaBoost, the random forest model has more advantages in predicting olfactory dysfunction in patients after transnasal sellar pituitary tumor resection, which is helpful for early identification and intervention of high-risk clinical population, and has good clinical application prospects.

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Bayesian effect selection in structured additive quantile regression

Rappl,

Carlan,

Kneib

et al. 2024

Statistical Modelling

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Bayesian structured additive quantile regression is an established tool for regressing outcomes with unknown distributions on a set of explanatory variables and/or when interest lies with effects on the more extreme values of the outcome. Even though variable selection for quantile regression exists, its scope is limited. We propose the use of the Normal Beta Prime Spike and Slab (NBPSS) prior in Bayesian quantile regression to aid the researcher in not only variable but also effect selection. We compare the Bayesian NBPSS approach to statistical boosting for quantile regression, a current standard in automated variable selection in quantile regression, in a simulation study with varying degrees of model complexity and illustrate both methods on an example of childhood malnutrition in Nigeria. The NBPSS prior shows good performance in variable and effect selection as well as prediction compared to boosting and can thus be recommended as an additional tool for quantile regression model building.

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Spike‐and‐slab least absolute shrinkage and selection operator generalized additive models and scalable algorithms for high‐dimensional data analysis

Cited by 3 publications

References 47 publications

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

Multi-omics characteristics of tumor-associated macrophages in the tumor microenvironment of gastric cancer and their exploration of immunotherapy potential

Establishment of a risk prediction model for olfactory disorders in patients with transnasal pituitary tumors by machine learning

Bayesian effect selection in structured additive quantile regression

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