A penalized linear mixed model with generalized method of moments for prediction analysis on high-dimensional multi-omics data.

With the advances in high-throughput biotechnologies, high-dimensional multi-layer omics data become increasingly available. They can provide both confirmatory and complementary information to disease risk and thus have offered unprecedented opportunities for risk prediction studies. However, the high-dimensionality and complex inter/intra-relationships among multi-omics data have brought tremendous analytical challenges. Here we present a computationally efficient penalized linear mixed model with generalized method of moments estimator (MpLMMGMM) for the prediction analysis on multi-omics data. Our method extends the widely used linear mixed model proposed for genomic risk predictions to model multi-omics data, where kernel functions are used to capture various types of predictive effects from different layers of omics data and penalty terms are introduced to reduce the impact of noise. Compared with existing penalized linear mixed models, the proposed method adopts the generalized method of moments estimator and it is much more computationally efficient. Through extensive simulation studies and the analysis of positron emission tomography imaging outcomes, we have demonstrated that MpLMMGMM can simultaneously consider a large number of variables and efficiently select those that are predictive from the corresponding omics layers. It can capture both linear and nonlinear predictive effects and achieves better prediction performance than competing methods.

Bayesian linear mixed model with multiple random effects for prediction analysis on high-dimensional multi-omics data.

MOTIVATION: Accurate disease risk prediction is an essential step in the modern quest for precision medicine. While high-dimensional multi-omics data have provided unprecedented data resources for prediction studies, their high-dimensionality and complex inter/intra-relationships have posed significant analytical challenges. RESULTS: We proposed a two-step Bayesian linear mixed model framework (TBLMM) for risk prediction analysis on multi-omics data. TBLMM models the predictive effects from multi-omics data using a hybrid of the sparsity regression and linear mixed model with multiple random effects. It can resemble the shape of the true effect size distributions and accounts for non-linear, including interaction effects, among multi-omics data via kernel fusion. It infers its parameters via a computationally efficient variational Bayes algorithm. Through extensive simulation studies and the prediction analyses on the positron emission tomography imaging outcomes using data obtained from the Alzheimer's Disease Neuroimaging Initiative, we have demonstrated that TBLMM can consistently outperform the existing method in predicting the risk of complex traits. AVAILABILITY AND IMPLEMENTATION: The corresponding R package is available on GitHub (https://github.com/YaluWen/TBLMM).

A penalized linear mixed model with generalized method of moments estimators for complex phenotype prediction.

MOTIVATION: Linear mixed models (LMMs) have long been the method of choice for risk prediction analysis on high-dimensional data. However, it remains computationally challenging to simultaneously model a large amount of variants that can be noise or have predictive effects of complex forms. RESULTS: In this work, we have developed a penalized LMM with generalized method of moments (pLMMGMM) estimators for prediction analysis. pLMMGMM is built within the LMM framework, where random effects are used to model the joint predictive effects from all variants within a region. Different from existing methods that focus on linear relationships and use empirical criteria for variable screening, pLMMGMM can efficiently detect regions that harbor genetic variants with both linear and non-linear predictive effects. In addition, unlike existing LMMs that can only handle a very limited number of random effects, pLMMGMM is much less computationally demanding. It can jointly consider a large number of regions and accurately detect those that are predictive. Through theoretical investigations, we have shown that our method has the selection consistency and asymptotic normality. Through extensive simulations and the analysis of PET-imaging outcomes, we have demonstrated that pLMMGMM outperformed existing models and it can accurately detect regions that harbor risk factors with various forms of predictive effects. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/XiaQiong/GMMLasso. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Explainable deep transfer learning model for disease risk prediction using high-dimensional genomic data.

Building an accurate disease risk prediction model is an essential step in the modern quest for precision medicine. While high-dimensional genomic data provides valuable data resources for the investigations of disease risk, their huge amount of noise and complex relationships between predictors and outcomes have brought tremendous analytical challenges. Deep learning model is the state-of-the-art methods for many prediction tasks, and it is a promising framework for the analysis of genomic data. However, deep learning models generally suffer from the curse of dimensionality and the lack of biological interpretability, both of which have greatly limited their applications. In this work, we have developed a deep neural network (DNN) based prediction modeling framework. We first proposed a group-wise feature importance score for feature selection, where genes harboring genetic variants with both linear and non-linear effects are efficiently detected. We then designed an explainable transfer-learning based DNN method, which can directly incorporate information from feature selection and accurately capture complex predictive effects. The proposed DNN-framework is biologically interpretable, as it is built based on the selected predictive genes. It is also computationally efficient and can be applied to genome-wide data. Through extensive simulations and real data analyses, we have demonstrated that our proposed method can not only efficiently detect predictive features, but also accurately predict disease risk, as compared to many existing methods.

A Bayesian linear mixed model for prediction of complex traits.

MOTIVATION: Accurate disease risk prediction is essential for precision medicine. Existing models either assume that diseases are caused by groups of predictors with small-to-moderate effects or a few isolated predictors with large effects. Their performance can be sensitive to the underlying disease mechanisms, which are usually unknown in advance. RESULTS: We developed a Bayesian linear mixed model (BLMM), where genetic effects were modelled using a hybrid of the sparsity regression and linear mixed model with multiple random effects. The parameters in BLMM were inferred through a computationally efficient variational Bayes algorithm. The proposed method can resemble the shape of the true effect size distributions, captures the predictive effects from both common and rare variants, and is robust against various disease models. Through extensive simulations and the application to a whole-genome sequencing dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we have demonstrated that BLMM has better prediction performance than existing methods and can detect variables and/or genetic regions that are predictive. AVAILABILITYAND IMPLEMENTATION: The R-package is available at https://github.com/yhai943/BLMM. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

A conditional autoregressive model for genetic association analysis accounting for genetic heterogeneity.

Converging evidence from genetic studies and population genetics theory suggest that complex diseases are characterized by remarkable genetic heterogeneity, and individual rare mutations with different effects could collectively play an important role in human diseases. Many existing statistical models for association analysis assume homogeneous effects of genetic variants across all individuals, and could be subject to power loss in the presence of genetic heterogeneity. To consider possible heterogeneous genetic effects among individuals, we propose a conditional autoregressive model. In the proposed method, the genetic effect is considered as a random effect and a score test is developed to test the variance component of genetic random effect. Through simulations, we compare the type I error and power performance of the proposed method with those of the generalized genetic random field and the sequence kernel association test methods under different disease scenarios. We find that our method outperforms the other two methods when (i) the rare variants have the major contribution to the disease, or (ii) the genetic effects vary in different individuals or subgroups of individuals. Finally, we illustrate the new method by applying it to the whole genome sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

A U-statistics for integrative analysis of multilayer omics data.

MOTIVATION: The emerging multilayer omics data provide unprecedented opportunities for detecting biomarkers that are associated with complex diseases at various molecular levels. However, the high-dimensionality of multiomics data and the complex disease etiologies have brought tremendous analytical challenges. RESULTS: We developed a U-statistics-based non-parametric framework for the association analysis of multilayer omics data, where consensus and permutation-based weighting schemes are developed to account for various types of disease models. Our proposed method is flexible for analyzing different types of outcomes as it makes no assumptions about their distributions. Moreover, it explicitly accounts for various types of underlying disease models through weighting schemes and thus provides robust performance against them. Through extensive simulations and the application to dataset obtained from the Alzheimer's Disease Neuroimaging Initiatives, we demonstrated that our method outperformed the commonly used kernel regression-based methods. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/Uomic. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Multi-kernel linear mixed model with adaptive lasso for prediction analysis on high-dimensional multi-omics data.

MOTIVATION: The use of human genome discoveries and other established factors to build an accurate risk prediction model is an essential step toward precision medicine. While multi-layer high-dimensional omics data provide unprecedented data resources for prediction studies, their corresponding analytical methods are much less developed. RESULTS: We present a multi-kernel penalized linear mixed model with adaptive lasso (MKpLMM), a predictive modeling framework that extends the standard linear mixed models widely used in genomic risk prediction, for multi-omics data analysis. MKpLMM can capture not only the predictive effects from each layer of omics data but also their interactions via using multiple kernel functions. It adopts a data-driven approach to select predictive regions as well as predictive layers of omics data, and achieves robust selection performance. Through extensive simulation studies, the analyses of PET-imaging outcomes from the Alzheimer's Disease Neuroimaging Initiative study, and the analyses of 64 drug responses, we demonstrate that MKpLMM consistently outperforms competing methods in phenotype prediction. AVAILABILITY AND IMPLEMENTATION: The R-package is available at https://github.com/YaluWen/OmicPred. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

[Analysis and considerations on revision of instructions for traditional Chinese medicine injections].

According to the notice on revision of the instructions for traditional Chinese medicine injections(TCMIs) issued by the National Medical Products Administration(NMPA) from January 2006 to May 2020, the revised contents in the instructions for 29 varieties involved in the notice were sorted out, and the existing problems in the instructions for TCMIs were analyzed, so as to provide the basis for dynamic revision of the instructions. It was found that the revised items of instructions for 29 varieties all involved adverse reactions, contraindications and precautions, and warnings were added for 82.76% of 29 TCMIs preparations, indicating that all the revised contents were related to safety issues. In addition, 33.33% of the drugs risks mentioned in the precautions were not indicated in the adverse reactions; 82.76% instructions did not indicate drug interactions; 17.24% instructions lacked medication notes for special populations; 48.28% instructions did not indicate traditional Chinese medicine(TCM) syndromes of the main disease; 44.83% instructions did not indicate the type and stage of indication; and 86.21% instructions did not indicate the course of treatment. It could be concluded that the instructions for TCMIs have known risks of drugs that are not fully reflected in adverse reactions and the effective information is not comprehensive. The risk control measures proposed in the precautions need to have aftereffect evaluation and there is a lack of drug interactions and medications for special populations. As an important part of the full life-cycle management of drugs, the revision of instructions for TCMIs should be continuously improved to provide the basis for safe and reasonable application of TCMIs. Based on the above problems, it is proposed that the marketing license holder as the main body of the revision of instructions should actively carry out post-marketing basic and clinical research in accordance with the characteristics of TCM, combine the updated research with the guidance of TCM theory and improve the revision level of instructions for TCMIs to provide the basis for post-marketing evaluation.

Multikernel linear mixed model with adaptive lasso for complex phenotype prediction.

Linear mixed models (LMMs) and their extensions have been widely used for high-dimensional genomic data analyses. While LMMs hold great promise for risk prediction research, the high dimensionality of the data and different effect sizes of genomic regions bring great analytical and computational challenges. In this work, we present a multikernel linear mixed model with adaptive lasso (KLMM-AL) to predict phenotypes using high-dimensional genomic data. We develop two algorithms for estimating parameters from our model and also establish the asymptotic properties of LMM with adaptive lasso when only one dependent observation is available. The proposed KLMM-AL can account for heterogeneous effect sizes from different genomic regions, capture both additive and nonadditive genetic effects, and adaptively and efficiently select predictive genomic regions and their corresponding effects. Through simulation studies, we demonstrate that KLMM-AL outperforms most of existing methods. Moreover, KLMM-AL achieves high sensitivity and specificity of selecting predictive genomic regions. KLMM-AL is further illustrated by an application to the sequencing dataset obtained from the Alzheimer's disease neuroimaging initiative.

Genetic risk prediction using a spatial autoregressive model with adaptive lasso.

With rapidly evolving high-throughput technologies, studies are being initiated to accelerate the process toward precision medicine. The collection of the vast amounts of sequencing data provides us with great opportunities to systematically study the role of a deep catalog of sequencing variants in risk prediction. Nevertheless, the massive amount of noise signals and low frequencies of rare variants in sequencing data pose great analytical challenges on risk prediction modeling. Motivated by the development in spatial statistics, we propose a spatial autoregressive model with adaptive lasso (SARAL) for risk prediction modeling using high-dimensional sequencing data. The SARAL is a set-based approach, and thus, it reduces the data dimension and accumulates genetic effects within a single-nucleotide variant (SNV) set. Moreover, it allows different SNV sets having various magnitudes and directions of effect sizes, which reflects the nature of complex diseases. With the adaptive lasso implemented, SARAL can shrink the effects of noise SNV sets to be zero and, thus, further improve prediction accuracy. Through simulation studies, we demonstrate that, overall, SARAL is comparable to, if not better than, the genomic best linear unbiased prediction method. The method is further illustrated by an application to the sequencing data from the Alzheimer's Disease Neuroimaging Initiative.

A Clustered Multiclass Likelihood-Ratio Ensemble Method for Family-Based Association Analysis Accounting for Phenotypic Heterogeneity.

Although compelling evidence suggests that the genetic etiology of complex diseases could be heterogeneous in subphenotype groups, little attention has been paid to phenotypic heterogeneity in genetic association analysis of complex diseases. Simply ignoring phenotypic heterogeneity in association analysis could result in attenuated estimates of genetic effects and low power of association tests if subphenotypes with similar clinical manifestations have heterogeneous underlying genetic etiologies. To facilitate the family-based association analysis allowing for phenotypic heterogeneity, we propose a clustered multiclass likelihood-ratio ensemble (CMLRE) method. The proposed method provides an alternative way to model the complex relationship between disease outcomes and genetic variants. It allows for heterogeneous genetic causes of disease subphenotypes and can be applied to various pedigree structures. Through simulations, we found CMLRE outperformed the commonly adopted strategies in a variety of underlying disease scenarios. We further applied CMLRE to a family-based dataset from the International Consortium to Identify Genes and Interactions Controlling Oral Clefts (ICOC) to investigate the genetic variants and interactions predisposing to subphenotypes of oral clefts. The analysis suggested that two subphenotypes, nonsyndromic cleft lip without palate (CL) and cleft lip with palate (CLP), shared similar genetic etiologies, while cleft palate only (CP) had its own genetic mechanism. The analysis further revealed that rs10863790 (IRF6), rs7017252 (8q24), and rs7078160 (VAX1) were jointly associated with CL/CLP, while rs7969932 (TBK1), rs227731 (17q22), and rs2141765 (TBK1) jointly contributed to CP.

Detection of differentially methylated regions in whole genome bisulfite sequencing data using local Getis-Ord statistics.

MOTIVATION: DNA methylation is an important epigenetic modification that has essential role in gene regulation, cell differentiation and cancer development. Bisulfite sequencing is a widely used technique to obtain genome-wide DNA methylation profiles, and one of the key tasks of analyzing bisulfite sequencing data is to detect differentially methylated regions (DMRs) among samples under different treatment conditions. Although numerous tools have been proposed to detect differentially methylated single CpG site (DMC) between samples, methods for direct DMR detection, especially for complex study designs, are largely limited. RESULTS: We present a new software, GetisDMR, for direct DMR detection. We use beta-binomial regression to model the whole-genome bisulfite sequencing data, where variations in methylation levels and confounding effects have been accounted for. We employ a region-wise test statistic, which is derived from local Getis-Ord statistics and considers the spatial correlation between nearby CpG sites, to detect DMRs. Unlike existing methods, that attempt to infer DMRs from DMCs based on empirical criteria, we provide statistical inference for direct DMR detection. Through extensive simulations and an application to two mouse datasets, we demonstrate that GetisDMR achieves better sensitivities, positive predictive values, more exact locations and better agreement of DMRs with current biological knowledge. AVAILABILITY AND IMPLEMENTATION: It is available at https://github.com/DMU-lilab/GetisDMR CONTACTS: y.wen@auckland.ac.nz or zhiguangli@dlmedu.edu.cnSupplementary information: Supplementary data are available at Bioinformatics online.

Detecting Gene-Gene Interactions Associated with Multiple Complex Traits with U-Statistics.

Many complex diseases, such as psychiatric and behavioral disorders, are commonly characterized through various measurements that reflect physical, behavioral and psychological aspects of diseases. While it remains a great challenge to find a unified measurement to characterize a disease, the available multiple phenotypes can be analyzed jointly in the genetic association study. Simultaneously testing these phenotypes has many advantages, including considering different aspects of the disease in the analysis, and utilizing correlated phenotypes to improve the power of detecting disease-associated variants. Furthermore, complex diseases are likely caused by the interplay of multiple genetic variants through complicated mechanisms. Considering gene-gene interactions in the joint association analysis of complex diseases could further increase our ability to discover genetic variants involving complex disease pathways. In this article, we propose a stepwise U-test for joint association analysis of multiple loci and multiple phenotypes. Through simulations, we demonstrated that testing multiple phenotypes simultaneously could attain higher power than testing one single phenotype at a time, especially when there are shared genes contributing to multiple phenotypes. We also illustrated the proposed method with an application to Nicotine Dependence (ND), using datasets from the Study of Addition, Genetics and Environment (SAGE). The joint analysis of three ND phenotypes identified two SNPs, rs10508649 and rs2491397, and reached a nominal P-value of 3.79e-13. The association was further replicated in two independent datasets with P-values of 2.37e-05 and 7.46e-05.

Project FIT: A School, Community and Social Marketing Intervention Improves Healthy Eating Among Low-Income Elementary School Children.

Project FIT was a two-year multi-component nutrition and physical activity intervention delivered in ethnically-diverse low-income elementary schools in Grand Rapids, MI. This paper reports effects on children's nutrition outcomes and process evaluation of the school component. A quasi-experimental design was utilized. 3rd, 4th and 5th-grade students (Yr 1 baseline: N = 410; Yr 2 baseline: N = 405; age range: 7.5-12.6 years) were measured in the fall and spring over the two-year intervention. Ordinal logistic, mixed effect models and generalized estimating equations were fitted, and the robust standard errors were utilized. Primary outcomes favoring the intervention students were found regarding consumption of fruits, vegetables and whole grain bread during year 2. Process evaluation revealed that implementation of most intervention components increased during year 2. Project FIT resulted in small but beneficial effects on consumption of fruits, vegetables, and whole grain bread in ethnically diverse low-income elementary school children.

The Michigan Healthy School Action Tools process generates improvements in school nutrition policies and practices, and student dietary intake.

The Michigan Healthy School Action Tools (HSAT) is an online self-assessment and action planning process for schools seeking to improve their health policies and practices. The School Nutrition Advances Kids study, a 2-year quasi-experimental intervention with low-income middle schools, evaluated whether completing the HSAT with a facilitator assistance and small grant funding resulted in (1) improvements in school nutrition practices and policies and (2) improvements in student dietary intake. A total of 65 low-income Michigan middle schools participated in the study. The Block Youth Food Frequency Questionnaire was completed by 1,176 seventh-grade students at baseline and in eighth grade (during intervention). Schools reported nutrition-related policies and practices/education using the School Environment and Policy Survey. Schools completing the HSAT were compared to schools that did not complete the HSAT with regard to number of policy and practice changes and student dietary intake. Schools that completed the HSAT made significantly more nutrition practice/education changes than schools that did not complete the HSAT, and students in those schools made dietary improvements in fruit, fiber, and cholesterol intake. The Michigan HSAT process is an effective strategy to initiate improvements in nutrition policies and practices within schools, and to improve student dietary intake.

A multiclass likelihood ratio approach for genetic risk prediction allowing for phenotypic heterogeneity.

The translation of human genome discoveries into health practice is one of the major challenges in the coming decades. The use of emerging genetic knowledge for early disease prediction, prevention, and pharmacogenetics will advance genome medicine and lead to more effective prevention/treatment strategies. For this reason, studies to assess the combined role of genetic and environmental discoveries in early disease prediction represent high priority research projects, as manifested in the multiple risk prediction studies now underway. However, the risk prediction models formed to date lack sufficient accuracy for clinical use. Converging evidence suggests that diseases with the same or similar clinical manifestations could have different pathophysiological and etiological processes. When heterogeneous subphenotypes are treated as a single entity, the effect size of predictors can be reduced substantially, leading to a low-accuracy risk prediction model. The use of more refined subphenotypes facilitates the identification of new predictors and leads to improved risk prediction models. To account for the phenotypic heterogeneity, we have developed a multiclass likelihood-ratio approach, which simultaneously determines the optimum number of subphenotype groups and builds a risk prediction model for each group. Simulation results demonstrated that the new approach had more accurate and robust performance than existing approaches under various underlying disease models. The empirical study of type II diabetes (T2D) by using data from the Genes and Environment Initiatives suggested heterogeneous etiology underlying obese and nonobese T2D patients. Considering phenotypic heterogeneity in the analysis leads to improved risk prediction models for both obese and nonobese T2D subjects.

A bivariate mann-whitney approach for unraveling genetic variants and interactions contributing to comorbidity.

Although comorbidity among complex diseases (e.g., drug dependence syndromes) is well documented, genetic variants contributing to the comorbidity are still largely unknown. The discovery of genetic variants and their interactions contributing to comorbidity will likely shed light on underlying pathophysiological and etiological processes, and promote effective treatments for comorbid conditions. For this reason, studies to discover genetic variants that foster the development of comorbidity represent high-priority research projects, as manifested in the behavioral genetics studies now underway. The yield from these studies can be enhanced by adopting novel statistical approaches, with the capacity of considering multiple genetic variants and possible interactions. For this purpose, we propose a bivariate Mann-Whitney (BMW) approach to unravel genetic variants and interactions contributing to comorbidity, as well as those unique to each comorbid condition. Through simulations, we found BMW outperformed two commonly adopted approaches in a variety of underlying disease and comorbidity models. We further applied BMW to datasets from the Study of Addiction: Genetics and Environment, investigating the contribution of 184 known nicotine dependence (ND) and alcohol dependence (AD) single nucleotide polymorphisms (SNPs) to the comorbidity of ND and AD. The analysis revealed a candidate SNP from CHRNA5, rs16969968, associated with both ND and AD, and replicated the findings in an independent dataset with a P-value of 1.06 × 10(-03) .

The impact of Bayesian optimization on feature selection.

Feature selection is an indispensable step for the analysis of high-dimensional molecular data. Despite its importance, consensus is lacking on how to choose the most appropriate feature selection methods, especially when the performance of the feature selection methods itself depends on hyper-parameters. Bayesian optimization has demonstrated its advantages in automatically configuring the settings of hyper-parameters for various models. However, it remains unclear whether Bayesian optimization can benefit feature selection methods. In this research, we conducted extensive simulation studies to compare the performance of various feature selection methods, with a particular focus on the impact of Bayesian optimization on those where hyper-parameters tuning is needed. We further utilized the gene expression data obtained from the Alzheimer's Disease Neuroimaging Initiative to predict various brain imaging-related phenotypes, where various feature selection methods were employed to mine the data. We found through simulation studies that feature selection methods with hyper-parameters tuned using Bayesian optimization often yield better recall rates, and the analysis of transcriptomic data further revealed that Bayesian optimization-guided feature selection can improve the accuracy of disease risk prediction models. In conclusion, Bayesian optimization can facilitate feature selection methods when hyper-parameter tuning is needed and has the potential to substantially benefit downstream tasks.

Safety and efficacy evaluation of Simo decoction and Arecae semen in herbal medicine practice.

Objective: The traditional Chinese patent medicine (TCPM), Simo decoction (Simo decoction oral solution), with its primary ingredient Arecae semen (Binglang, Areca catechu L.), known for its potential carcinogenic effects, is the subject of this study. The research aims to analyze the effectiveness and potential risks of Simo decoction, particularly as a carcinogen, and to suggest a framework for evaluating the risks and benefits of other herbal medicines. Methods: The study is based on post-marketing research of Simo decoction and Arecae semen. It utilized a wide range of sources, including ancient and modern literature, focusing on the efficacy and safety of Simo decoction. The research includes retrospective data on the sources, varieties, and toxicological studies of Arecae semen from databases such as Pubmed, Clinical Trials, Chinese Clinical Trial Registry, China National Knowledge Infrastructure, WHO-UMC Vigibase, and China National Center for ADR Monitoring. Results: Common adverse drug reactions (ADRs) associated with Simo decoction include skin rash, nausea, vomiting, abdominal pain, and diarrhea. However, no studies exist reporting the severe ADRs, such as carcinogenic effects. Arecae semen is distributed across approximately 60 varieties in tropical Asia and Australia. According to the WHO-UMC Vigibase and the National Adverse Drug Reaction Monitoring System databases, there are currently no reports of toxicity related to Arecae semen in the International System for Classification of ADRs (ISCR) or clinical studies. Conclusion: Risk-benefit analysis in TCPM presents more challenges compared to conventional drugs. The development of a practical pharmacovigilance system and risk-benefit analysis framework is crucial for marketing authorization holders, researchers, and regulatory bodies. This approach is vital for scientific supervision and ensuring the safety and efficacy of drug applications, thus protecting public health.