Data science in science最新文献

Biclustering Multivariate Longitudinal Data with Application to Recovery Trajectories of White Matter After Sport-Related Concussion 双聚类多变量纵向数据在运动相关脑震荡后白质恢复轨迹中的应用

Data science in science Pub Date : 2024-07-16 DOI: 10.1080/26941899.2024.2376535

Caleb Weaver, Luo Xiao, Qiuting Wen, Yu-Chien Wu, Jaroslaw Harezlak

引用次数: 0

Model Selection for Exposure-Mediator Interaction. 暴露-中介相互作用的模型选择。

Data science in science Pub Date : 2024-01-01 Epub Date: 2024-06-16 DOI: 10.1080/26941899.2024.2360892

Ruiyang Li, Xi Zhu, Seonjoo Lee

{"title":"Model Selection for Exposure-Mediator Interaction.","authors":"Ruiyang Li, Xi Zhu, Seonjoo Lee","doi":"10.1080/26941899.2024.2360892","DOIUrl":"10.1080/26941899.2024.2360892","url":null,"abstract":"In mediation analysis, the exposure often influences the mediating effect, i.e., there is an interaction between exposure and mediator on the dependent variable. When the mediator is high-dimensional, it is necessary to identify non-zero mediators <math> <mrow><mfenced><mi>M</mi></mfenced> </mrow> </math> and exposure-by-mediator ( <math><mi>X</mi></math> -by- <math><mi>M</mi></math> ) interactions. Although several high-dimensional mediation methods can naturally handle <math><mi>X</mi></math> -by- <math><mi>M</mi></math> interactions, research is scarce in preserving the underlying hierarchical structure between the main effects and the interactions. To fill the knowledge gap, we develop the XMInt procedure to select <math><mi>M</mi></math> and <math><mi>X</mi></math> -by- <math><mi>M</mi></math> interactions in the high-dimensional mediators setting while preserving the hierarchical structure. Our proposed method employs a sequential regularization-based forward-selection approach to identify the mediators and their hierarchically preserved interaction with exposure. Our numerical experiments showed promising selection results. Further, we applied our method to ADNI morphological data and examined the role of cortical thickness and subcortical volumes on the effect of amyloid-beta accumulation on cognitive performance, which could be helpful in understanding the brain compensation mechanism.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11210705/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141473202","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits. 利用多变量反应的混合回归发现具有检测限的阿尔茨海默氏症生物标志物亚型。

Data science in science Pub Date : 2024-01-01 Epub Date: 2024-03-06 DOI: 10.1080/26941899.2024.2309403

Ganzhong Tian, John Hanfelt, James Lah, Benjamin B Risk

{"title":"Mixture of regressions with multivariate responses for discovering subtypes in Alzheimer's biomarkers with detection limits.","authors":"Ganzhong Tian, John Hanfelt, James Lah, Benjamin B Risk","doi":"10.1080/26941899.2024.2309403","DOIUrl":"https://doi.org/10.1080/26941899.2024.2309403","url":null,"abstract":"There is no gold standard for the diagnosis of Alzheimer's disease (AD), except from autopsies, which motivates the use of unsupervised learning. A mixture of regressions is an unsupervised method that can simultaneously identify clusters from multiple biomarkers while learning within-cluster demographic effects. Cerebrospinal fluid (CSF) biomarkers for AD have detection limits, which create additional challenges. We apply a mixture of regressions with a multivariate truncated Gaussian distribution (also called a censored multivariate Gaussian mixture of regressions or a mixture of multivariate tobit regressions) to over 3,000 participants from the Emory Goizueta Alzheimer's Disease Research Center and Emory Healthy Brain Study to examine amyloid-beta peptide 1-42 (Abeta42), total tau protein and phosphorylated tau protein in CSF with known detection limits. We address three gaps in the literature on mixture of regressions with a truncated multivariate Gaussian distribution: software availability; inference; and clustering accuracy. We discovered three clusters that tend to align with an AD group, a normal control profile and non-AD pathology. The CSF profiles differed by race, gender and the genetic marker ApoE4, highlighting the importance of considering demographic factors in unsupervised learning with detection limits. Notably, African American participants in the AD-like group had significantly lower tau burden.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11044119/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140869637","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Rewiring Dynamics of Functional Connectomes during Motor-Skill Learning 运动技能学习过程中功能连接体的重新布线动态

Data science in science Pub Date : 2023-10-05 DOI: 10.1080/26941899.2023.2260431

Saber Meamardoost, Mahasweta Bhattacharya, Eun Jung Hwang, Chi Ren, Linbing Wang, Claudia Mewes, Ying Zhang, Takaki Komiyama, Rudiyanto Gunawan

{"title":"Rewiring Dynamics of Functional Connectomes during Motor-Skill Learning","authors":"Saber Meamardoost, Mahasweta Bhattacharya, Eun Jung Hwang, Chi Ren, Linbing Wang, Claudia Mewes, Ying Zhang, Takaki Komiyama, Rudiyanto Gunawan","doi":"10.1080/26941899.2023.2260431","DOIUrl":"https://doi.org/10.1080/26941899.2023.2260431","url":null,"abstract":"The brain’s functional connectome continually rewires throughout an organism’s life. In this study, we sought to elucidate the operational principles of such rewiring in mouse primary motor cortex (M1) by analyzing calcium imaging of layer 2/3 (L2/3) and layer 5 (L5) neuronal activity in M1 of awake mice during a lever-press task learning. Our results show that L2/3 and L5 functional connectomes follow a similar learning-induced rewiring trajectory. More specifically, the connectomes rewire in a biphasic manner, where functional connectivity increases over the first few learning sessions, and then, it is gradually pruned to return to a homeostatic level of network density. We demonstrated that the increase of network connectivity in L2/3 connectomes, but not in L5, generates neuronal co-firing activity that correlates with improved motor performance (shorter cue-to-reward time), while motor performance remains relatively stable throughout the pruning phase. The results show a biphasic rewiring principle that involves the maximization of reward/performance and maintenance of network density. Finally, we demonstrated that the connectome rewiring in L2/3 is clustered around a core set of movement-associated neurons that form a highly interconnected hub in the connectomes, and that the activity of these core neurons stably encodes movement throughout learning.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134975618","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

An Empirical Bayes Approach to Estimating Dynamic Models of Co-Regulated Gene Expression 用经验贝叶斯方法估计共调控基因表达的动态模型

Data science in science Pub Date : 2023-08-21 DOI: 10.1080/26941899.2023.2219707

Sara Venkatraman, Sumanta Basu, Andrew G. Clark, Sofie Delbare, Myung Hee Lee, Martin T. Wells

{"title":"An Empirical Bayes Approach to Estimating Dynamic Models of Co-Regulated Gene Expression","authors":"Sara Venkatraman, Sumanta Basu, Andrew G. Clark, Sofie Delbare, Myung Hee Lee, Martin T. Wells","doi":"10.1080/26941899.2023.2219707","DOIUrl":"https://doi.org/10.1080/26941899.2023.2219707","url":null,"abstract":"Time-course gene expression datasets provide insight into the dynamics of complex biological processes, such as immune response and organ development. It is of interest to identify genes with similar temporal expression patterns because such genes are often biologically related. However, this task is challenging due to the high dimensionality of these datasets and the nonlinearity of gene expression time dynamics. We propose an empirical Bayes approach to estimating ordinary differential equation (ODE) models of gene expression, from which we derive a similarity metric between genes called the Bayesian lead-lag R2 (LLR2). Importantly, the calculation of the LLR2 leverages biological databases that document known interactions amongst genes; this information is automatically used to define informative prior distributions on the ODE model’s parameters. As a result, the LLR2 is a biologically-informed metric that can be used to identify clusters or networks of functionally-related genes with co-moving or time-delayed expression patterns. We then derive data-driven shrinkage parameters from Stein’s unbiased risk estimate that optimally balance the ODE model’s fit to both data and external biological information. Using real gene expression data, we demonstrate that our methodology allows us to recover interpretable gene clusters and sparse networks. These results reveal new insights about the dynamics of biological systems.","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135820909","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

Evaluation of Seasonality in Sea Surface Salinity Balance Equation via Function Registration 用函数配准评价海面盐度平衡方程的季节性

Data science in science Pub Date : 2023-07-19 DOI: 10.1080/26941899.2023.2231061

Yoonji Kim, S. Brodnitz, O. Chkrebtii, F. Bingham

引用次数: 0

Data Science in Science: Special Issue on Data Science in the Brain Sciences 科学中的数据科学：《脑科学》数据科学特刊

Data science in science Pub Date : 2023-07-07 DOI: 10.1080/26941899.2023.2216814

Carolina Euán, M. Fiecas, H. Ombao, D. Matteson

引用次数: 0

Adaptive Online Multivariate Signal Extraction With Locally Weighted Robust Polynomial Regression 基于局部加权鲁棒多项式回归的自适应在线多变量信号提取

Data science in science Pub Date : 2023-05-10 DOI: 10.1080/26941899.2023.2200856

M. Klanderman, Junho Lee, K. Villez, T. Cath, A. Hering

引用次数: 0

News-Based Sparse Machine Learning Models for Adaptive Asset Pricing 基于新闻的自适应资产定价稀疏机器学习模型

Data science in science Pub Date : 2023-04-03 DOI: 10.1080/26941899.2023.2187895

Liao Zhu, Haoxuan Wu, M. Wells

引用次数: 0

Measuring and Quantifying Uncertainty in Volatility Spillovers: A Bayesian Approach 波动性溢出的不确定性度量与量化：贝叶斯方法

Data science in science Pub Date : 2023-03-06 DOI: 10.1080/26941899.2023.2176379

Yu. P. Shapovalova, M. Eichler

引用次数: 1