Statistics in Biosciences最新文献_第3页

Tweedie Distributions for Biological Sequences Alignments 生物序列比对的Tweedie分布

Statistics in Biosciences Pub Date : 2023-10-09 DOI: 10.1007/s12561-023-09388-4

Ben Hassen Hanen, Masmoudi Khalil, Masmoudi Afif

引用次数: 0

Unlocking Cellular Insights Through Cell-Type Decomposition 通过细胞类型分解解锁细胞洞察力

Statistics in Biosciences Pub Date : 2023-10-04 DOI: 10.1007/s12561-023-09389-3

Xiaoyu Song

引用次数: 0

Power Analysis of Exposure Mixture Studies Via Monte Carlo Simulations 基于蒙特卡罗模拟的暴露混合物功率分析研究

Statistics in Biosciences Pub Date : 2023-10-01 DOI: 10.1007/s12561-023-09385-7

Phuc H. Nguyen, Amy H. Herring, Stephanie M. Engel

引用次数: 1

Improving the Power to Detect Indirect Effects in Mediation Analysis 提高中介分析中间接影响的检测能力

Statistics in Biosciences Pub Date : 2023-09-27 DOI: 10.1007/s12561-023-09386-6

John Kidd, Dan-Yu Lin

引用次数: 0

Detecting Disease Outbreak Regions Using Multiple Data Streams 使用多个数据流检测疾病爆发区域

Statistics in Biosciences Pub Date : 2023-09-16 DOI: 10.1007/s12561-023-09387-5

Sesha Dassanayake, Joshua P. French

引用次数: 0

Mediation Analysis with Random Distribution as Mediator with an Application to iCOMPARE Trial 随机分布作为中介的中介分析及其在iccompare试验中的应用

IF 1

Statistics in Biosciences Pub Date : 2023-08-30 DOI: 10.1007/s12561-023-09383-9

Jingru Zhang, M. Basner, Christopher W Jones, D. Dinges, H. Shou, Hongzhe Li

引用次数: 0

Simultaneous Denoising and Heterogeneity Learning for Time Series Data 时间序列数据的同时去噪和异质性学习

IF 1

Statistics in Biosciences Pub Date : 2023-08-24 DOI: 10.1007/s12561-023-09384-8

Xiwen Jiang, Weining Shen

引用次数: 0

Understanding Effective Virus Control Policies for Covid-19 with the Q-learning Method 用q -学习方法理解Covid-19有效的病毒控制策略

IF 1

Statistics in Biosciences Pub Date : 2023-08-11 DOI: 10.1007/s12561-023-09382-w

Yasin Khadem Charvadeh, G. Yi

引用次数: 0

Semiparametric Trend Analysis for Stratified Recurrent Gap Times Under Weak Comparability Constraint. 弱可比性约束下分层循环间隙时间的半参数趋势分析

IF 0.8

Statistics in Biosciences Pub Date : 2023-07-01 Epub Date: 2023-06-03 DOI: 10.1007/s12561-023-09376-8

Peng Liu, Yijian Huang, Kwun Chuen Gary Chan, Ying Qing Chen

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引用次数: 0

Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data 大规模遗传数据的统计学习:如何使用1000基因组计划数据进行基因表达数据的全基因组关联研究

Statistics in Biosciences Pub Date : 2023-07-01 DOI: 10.1007/s12561-023-09375-9

Anton Sugolov, Eric Emmenegger, Andrew D. Paterson, Lei Sun

{"title":"Statistical Learning of Large-Scale Genetic Data: How to Run a Genome-Wide Association Study of Gene-Expression Data Using the 1000 Genomes Project Data","authors":"Anton Sugolov, Eric Emmenegger, Andrew D. Paterson, Lei Sun","doi":"10.1007/s12561-023-09375-9","DOIUrl":"https://doi.org/10.1007/s12561-023-09375-9","url":null,"abstract":"Abstract Teaching statistics through engaging applications to contemporary large-scale datasets is essential to attracting students to the field. To this end, we developed a hands-on, week-long workshop for senior high-school or junior undergraduate students, without prior knowledge in statistical genetics but with some basic knowledge in data science, to conduct their own genome-wide association study (GWAS). The GWAS was performed for open source gene expression data, using publicly available human genetics data. Assisted by a detailed instruction manual, students were able to obtain $$sim$$ <mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\"> <mml:mo>∼</mml:mo> </mml:math> 1.4 million p-values from a real scientific study, within several days. This early motivation kept students engaged in learning the theories that support their results, including regression, data visualization, results interpretation, and large-scale multiple hypothesis testing. To further their learning motivation by emphasizing the personal connection to this type of data analysis, students were encouraged to make short presentations about how GWAS has provided insights into the genetic basis of diseases that are present in their friends or families. The appended open source, step-by-step instruction manual includes descriptions of the datasets used, the software needed, and results from the workshop. Additionally, scripts used in the workshop are archived on Github and Zenodo to further enhance reproducible research and training.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135315171","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0