Statistical Applications in Genetics and Molecular Biology最新文献

Heteroscedastic nearest shrunken centroid classification: methods and applications to high-dimensional data. 异方差最近萎缩质心分类：高维数据的方法和应用。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-11-02 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2026-0028

Mohammad Omar Sahtout, Haiyan Wang

{"title":"Heteroscedastic nearest shrunken centroid classification: methods and applications to high-dimensional data.","authors":"Mohammad Omar Sahtout, Haiyan Wang","doi":"10.1515/sagmb-2026-0028","DOIUrl":"https://doi.org/10.1515/sagmb-2026-0028","url":null,"abstract":"Despite of recent advances in machine learning methods with huge training data, classification of high-dimensional, low-sample-size (HDLSS) data remains challenging because limited sample sizes increase the risk of overfitting and hinder reliable parameter estimation. The nearest shrunken centroid classifier, also known as Prediction Analysis of Microarrays (PAM), combines centroid-based discrimination with feature selection through thresholding. However, its homoscedasticity assumption may limit classification performance when feature variances differ across classes, as commonly observed in gene expression data. We propose a heteroscedastic extension of the nearest shrunken centroid framework that incorporates class-specific variances into both the test statistic and discriminant function. The proposed method retains the simplicity and interpretability of the original classifier while accommodating variance heterogeneity. By integrating the framework with soft, hard, and order thresholding strategies, we develop three new classifiers. We evaluate their performance through comprehensive simulation studies and applications to multiple multiclass cancer gene expression datasets. The results show that accounting for heteroscedasticity substantially improves classification accuracy, particularly with hard and order thresholding, while maintaining competitive feature sparsity. These findings highlight the importance of modeling variance heterogeneity in high-dimensional classification and provide practical tools for improved cancer subtype prediction.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149326689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A research on machine learning based gene selection algorithms for RNA-seq data. 基于机器学习的RNA-seq数据基因选择算法研究。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-10-16 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0074

Merve Kasikci, Osman Dag

{"title":"A research on machine learning based gene selection algorithms for RNA-seq data.","authors":"Merve Kasikci, Osman Dag","doi":"10.1515/sagmb-2025-0074","DOIUrl":"https://doi.org/10.1515/sagmb-2025-0074","url":null,"abstract":"Discovering differentially expressed genes (DEGs) is an essential process for diagnosis and treatment. In this study, we evaluate the capacity of machine learning algorithms to identify genes that are simulated as truly differentially expressed. We first obtain nine real datasets from The Cancer Genome Atlas Program. We conduct Monte Carlo simulation by exploring the characteristics of the real datasets (i.e. the number of genes). Since these datasets have highly imbalanced class distributions, datasets with the same characteristics are also generated with balanced class ratios. In this way, 18 scenarios are obtained with a total number of 19,947 genes of which 10 genes are simulated significantly different between the two groups. After the pre-processing steps including filtering, normalization, transformation and univariate analysis, gene selection is conducted with five machine learning algorithms. These are biosigner algorithm, GMDH-type neural network algorithm, Determan's optimal gene selection algorithm with random forest, support vector machines and elastic net generalized linear models. Five-fold cross validation is used for improving gene selection performances. Recall, precision and F-measure are taken into account for the evaluation of gene selection performances. All algorithms show higher average performance in balanced scenarios than in unbalanced scenarios. Elastic net generally achieves favorable average performance in both balanced and unbalanced scenarios.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149197238","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A statistical review of polygenic risk scores: from heuristics to model-based inference. 多基因风险评分的统计回顾：从启发式到基于模型的推理。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-09-21 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2026-0007

Xuan Huang, Wei Jiang

{"title":"A statistical review of polygenic risk scores: from heuristics to model-based inference.","authors":"Xuan Huang, Wei Jiang","doi":"10.1515/sagmb-2026-0007","DOIUrl":"https://doi.org/10.1515/sagmb-2026-0007","url":null,"abstract":"Polygenic risk scores (PRS) were initially developed as pragmatic tools to aggregate genome-wide association study (GWAS) signals for individual-level prediction, relying on heuristic strategies such as clumping and thresholding to approximate independence among variants. Although computationally efficient and widely accessible, these early approaches were sensitive to tuning parameters and limited in their ability to capture the diffuse signal characteristic of highly polygenic traits. As GWAS sample sizes expanded and biobank-scale resources emerged, methodological priorities shifted toward statistically principled models that explicitly represent linkage disequilibrium, effect-size heterogeneity, and population structure. In this review, we examine the methodological evolution of PRS construction from threshold-based aggregation to fully model-based inference frameworks, including linear mixed models, LD-aware Bayesian shrinkage approaches, machine learning, and recent multi-ancestry extensions, and summarize practical considerations for method selection under different data-access, LD-reference, tuning, and ancestry settings. Collectively, these developments mark a transition from heuristic scoring algorithms to a mature, statistically grounded paradigm for genomic risk prediction.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-09-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"149002863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A bi-directional Mendelian randomization study of sarcopenia-related traits and renal function. 肌少症相关特征与肾功能的双向孟德尔随机研究。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-09-14 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0058

Xiaoyan Zhou, Yan Sun, Yujie Hu, Guoshuai Yang

{"title":"A bi-directional Mendelian randomization study of sarcopenia-related traits and renal function.","authors":"Xiaoyan Zhou, Yan Sun, Yujie Hu, Guoshuai Yang","doi":"10.1515/sagmb-2025-0058","DOIUrl":"https://doi.org/10.1515/sagmb-2025-0058","url":null,"abstract":"The association between sarcopenia and renal function has been reported in observational studies; however, the directionality and potential causal nature of these associations remain uncertain. We assessed whether genetically predicted sarcopenia-related traits are associated with renal function and vice versa using bidirectional Mendelian randomization (MR). We conducted a bidirectional two-sample MR analysis using publicly available European-ancestry GWAS summary statistics for appendicular lean mass (ALM), hand-grip strength (left and right), and walking pace, and for renal function (cystatin C-based estimated glomerular filtration rate [eGFRcystatin C] and urinary albumin excretion [UAE]). Causal estimates were primarily obtained using inverse-variance weighted (IVW) models, complemented by sensitivity analyses (MR-Egger intercept, weighted median/mode, MR-PRESSO, Radial MR, and leave-one-out). In forward MR, genetically predicted walking pace was positively associated with eGFRcystatin C. Genetically predicted ALM and grip strength (right and left) were inversely associated with UAE. In reverse MR, genetically predicted UAE was inversely associated with ALM and right-hand grip strength. Estimates were broadly consistent across sensitivity analyses, and outlier-robust analyses (MR-PRESSO/Radial MR) yielded similar results. These findings provide genetic evidence consistent with bidirectional relationships between sarcopenia-related traits and renal function (particularly UAE), under standard MR assumptions. Given potential limitations (e.g., heterogeneity, pleiotropy, and possible sample overlap), the results should be interpreted cautiously and complemented by other lines of evidence.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-09-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148946124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Methods for modeling gene-environment interplay using polygenic risk scores. 使用多基因风险评分来模拟基因-环境相互作用的方法。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-09-02 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2026-0006

Ziqiao Wang

引用次数: 0

A hybrid deep learning framework for WT or mutant peptide prediction using p53 mutation data. 使用p53突变数据进行WT或突变肽预测的混合深度学习框架。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-08-07 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0078

Manisha R Patil, Anand Bihari

{"title":"A hybrid deep learning framework for WT or mutant peptide prediction using p53 mutation data.","authors":"Manisha R Patil, Anand Bihari","doi":"10.1515/sagmb-2025-0078","DOIUrl":"https://doi.org/10.1515/sagmb-2025-0078","url":null,"abstract":"p53 is a tumor suppressor protein that maintains genome integrity. Single amino acid variations (SAVs), especially hotspot mutations (such as R175 and R248), are closely associated with oncogenic transformation and weaken DNA-binding and transcriptional regulatory capabilities. To identify deleterious SAVs for understanding disease mechanisms and key cellular processes, including apoptosis, DNA repair, and cell cycle regulation. This study used quantitative biochemical and microbial descriptors of p53 peptide sequences, along with a CNN and a 2-layer bidirectional long short-term memory (Bi-LSTM) network architecture with attention mechanisms and ESM-2-based embeddings, to classify sequences as wild-type or mutant. A systematic analysis of the p53 mutation dataset was performed using molecular weight, instability index, hydrophobicity, motif enrichment, and amino acid substitution patterns to identify mutation-centered peptides. Using stratified 5-fold cross-validation, the proposed model was tested, achieving an accuracy of 0.98, an area under the ROC curve (AUROC) of 0.98, and a precision-recall AUC of 0.99. Furthermore, SHAP-based interpretation identified the key amino acid residues and biochemical factors that contribute to the model's predictive performance. The proposed approach provides insights into the structural, sequential, and biochemical effects of variants, an interpretable, robust framework for evaluating the functional consequences of p53 hotspot and other variants, and a computationally efficient tool for prioritizing high-risk p53 variants.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148686324","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Balanced mediated pathway detection in genomic data. 基因组数据中的平衡介导通路检测。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-07-02 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0068

Joseph Boccardo, William Tanberg, David Tritchler, Jeffrey Miecznikowski

{"title":"Balanced mediated pathway detection in genomic data.","authors":"Joseph Boccardo, William Tanberg, David Tritchler, Jeffrey Miecznikowski","doi":"10.1515/sagmb-2025-0068","DOIUrl":"https://doi.org/10.1515/sagmb-2025-0068","url":null,"abstract":"Researchers are increasingly interested in identifying different parts of the genome which work together to influence a phenotypic trait. A major objective in bioinformatics involves finding groups of variables determined from omics technologies such as DNA methylation sites, transcriptome profiling, etc. Given one set of variables, one could determine how variables within work together to influence an outcome. These groups of variables are called functional modules and previous work has identified them through sparse matrix decomposition techniques such as sparse principal components analysis. To determine how different parts of the genome work together, we present methods to extend functional modules and identify variables that influence an outcome variable through a stepwise mediating fashion. Traditionally, module discovery involves sparse matrix decomposition accomplished through tuning regularization constraints. In this paper, we efficiently tune a cardinality-based sparse singular value decomposition to discover balanced mediated functional modules. These methods will be tested on simulated stepwise functional modules that contain several signal and non-signal variables and applied to real omics data collected in The Cancer Genome Atlas.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148370435","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Annealed variational mixtures for disease subtyping and biomarker discovery. 疾病分型和生物标志物发现的退火变异混合物。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-05-22 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0001

Emma Prevot, Rory Toogood, Filippo Pagani, Paul D W Kirk

引用次数: 0

Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient. 基于碱基调用误差的排列测试方法用于检测单个患者ctDNA中变异等位基因频率的变化。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-05-20 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2024-0020

Aya Kuchiba, Jo Nishino, Shinichi Yachida, Kuniko Sunami, Takafumi Koyama, Noboru Yamamoto, Mamoru Kato, Taro Shibata

{"title":"Performance of the permutation test approach with base calling errors for detecting changes in variant allele frequencies in ctDNA for a single patient.","authors":"Aya Kuchiba, Jo Nishino, Shinichi Yachida, Kuniko Sunami, Takafumi Koyama, Noboru Yamamoto, Mamoru Kato, Taro Shibata","doi":"10.1515/sagmb-2024-0020","DOIUrl":"10.1515/sagmb-2024-0020","url":null,"abstract":"Serial measurements of circulating tumor DNA (ctDNA) with next-generation sequencing (NGS) technology enable the real-time, noninvasive evaluation of longitudinal changes of molecular profiles in tumor tissues. These profiles can assist in monitoring disease progression and tumor response to targeted therapies, thereby facilitating informed therapeutic decision making through precise molecular characterization. While base calling errors are inherent in detecting ctDNA mutations, there is limited consideration of the effect of base calling errors on detecting variant allele frequency (VAF) changes. This study introduces the likelihood functions that incorporate the base calling error probabilities and proposes the permutation test for detecting VAF changes between two time points for a patient. Simulation studies, with scenarios based on real NGS analysis data from the TOP-GEAR project, showed that the proposed test maintained the nominal significance level at two-sided 0.05 regardless of the base calling error probabilities and VAFs. In our scenarios with 500 depth at each time point, the power of 80 % was achievable for a twofold or more increase when the VAF was 0.05 at baseline and a half or more decrease when the VAF was 0.1. Our empirical results show the utility and potential of the permutation test to detect VAF changes.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976696","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

BLOG: Bayesian longitudinal omics with group constraints. 博客：具有群体约束的贝叶斯纵向组学。

IF 0.4 4区数学

Statistical Applications in Genetics and Molecular Biology Pub Date : 2026-05-19 eCollection Date: 2026-01-01 DOI: 10.1515/sagmb-2025-0039

Livia Popa, Sumanta Basu, Myung Hee Lee, Martin T Wells

{"title":"BLOG: Bayesian longitudinal omics with group constraints.","authors":"Livia Popa, Sumanta Basu, Myung Hee Lee, Martin T Wells","doi":"10.1515/sagmb-2025-0039","DOIUrl":"10.1515/sagmb-2025-0039","url":null,"abstract":"Clinical investigators are increasingly interested in discovering computational biomarkers from short-term longitudinal omics datasets. This work focuses on Bayesian regression and variable selection for longitudinal omics datasets, which can estimate posterior evidence for each candidate biomarker and control false discovery using evidence thresholds. In our univariate approach, Zellner's g-prior is used with two different options of the tuning parameter g: <math><mi>g</mi> <mo>=</mo> <msqrt><mrow><mi>n</mi></mrow> </msqrt> </math> and a g that minimizes Stein's unbiased risk estimate (SURE). Bayes factors are used to rank metabolites by evidence, and their mapping to posterior model probabilities under prior odds can provide local posterior null probabilities whose average over the rejection set defines a Bayesian analogue of the global FDR. In the multivariate approach, we use Bayesian group lasso with a spike and slab prior for group variable selection. In both approaches, we use the first difference (Δ) scale of longitudinal predictor and the response. These methods work together to enhance our understanding of biomarker identification, improving inference and prediction. We compare our method against commonly used linear mixed effect models on simulated data and real data from a Tuberculosis (TB) study on metabolite biomarker selection. With an automated selection of hyperparameters, the Zellner's g-prior approach correctly identifies target metabolites with high specificity and sensitivity across various simulation and real data scenarios. The multivariate Bayesian group lasso spike and slab approach also correctly selects target metabolites across various simulation scenarios.","PeriodicalId":49477,"journal":{"name":"Statistical Applications in Genetics and Molecular Biology","volume":"25 1","pages":""},"PeriodicalIF":0.4,"publicationDate":"2026-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147976674","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0