Pub Date : 2024-02-07eCollection Date: 2024-06-01DOI: 10.1093/jrsssc/qlae006
Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez
We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).
我们考虑通过一个潜在的多项式变量进行无监督分类，该变量将标量响应归类到包含标量和函数协变量的混合物模型的 L 个分量之一。这一过程可视为一个分层模型，第一层根据参数分布的混合物对标量响应进行建模，第二层通过包含功能和标量协变量的广义线性模型对混合物概率进行建模。将函数协变量视为向量的传统方法不仅存在维度诅咒，因为函数协变量的测量间隔可能非常小，导致模型高度参数化，而且没有考虑到数据的性质。我们使用基扩展来降低维度，并使用贝叶斯方法来估计参数，同时提供潜在分类向量的预测。该方法由两个现有方法不易处理的数据实例激发。第一个例子涉及识别临床试验中的安慰剂应答者（正态混合模型），另一个例子涉及预测挤奶奶牛的疾病（泊松模型的零膨胀混合）。
{"title":"Unsupervised Bayesian classification for models with scalar and functional covariates.","authors":"Nancy L Garcia, Mariana Rodrigues-Motta, Helio S Migon, Eva Petkova, Thaddeus Tarpey, R Todd Ogden, Julio O Giordano, Martin M Perez","doi":"10.1093/jrsssc/qlae006","DOIUrl":"10.1093/jrsssc/qlae006","url":null,"abstract":"<p><p>We consider unsupervised classification by means of a latent multinomial variable which categorizes a scalar response into one of the L components of a mixture model which incorporates scalar and functional covariates. This process can be thought as a hierarchical model with the first level modelling a scalar response according to a mixture of parametric distributions and the second level modelling the mixture probabilities by means of a generalized linear model with functional and scalar covariates. The traditional approach of treating functional covariates as vectors not only suffers from the curse of dimensionality, since functional covariates can be measured at very small intervals leading to a highly parametrized model, but also does not take into account the nature of the data. We use basis expansions to reduce the dimensionality and a Bayesian approach for estimating the parameters while providing predictions of the latent classification vector. The method is motivated by two data examples that are not easily handled by existing methods. The first example concerns identifying placebo responders on a clinical trial (normal mixture model) and the other predicting illness for milking cows (zero-inflated mixture of the Poisson model).</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 3","pages":"658-681"},"PeriodicalIF":1.0,"publicationDate":"2024-02-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271982/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2024-02-01eCollection Date: 2024-06-01DOI: 10.1093/jrsssc/qlae003
Xinyuan Tian, Maria Ciarleglio, Jiachen Cai, Erich J Greene, Denise Esserman, Fan Li, Yize Zhao
Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.
{"title":"Bayesian semi-parametric inference for clustered recurrent events with zero inflation and a terminal event.","authors":"Xinyuan Tian, Maria Ciarleglio, Jiachen Cai, Erich J Greene, Denise Esserman, Fan Li, Yize Zhao","doi":"10.1093/jrsssc/qlae003","DOIUrl":"10.1093/jrsssc/qlae003","url":null,"abstract":"<p><p>Recurrent events are common in clinical studies and are often subject to terminal events. In pragmatic trials, participants are often nested in clinics and can be susceptible or structurally unsusceptible to the recurrent events. We develop a Bayesian shared random effects model to accommodate this complex data structure. To achieve robustness, we consider the Dirichlet processes to model the residual of the accelerated failure time model for the survival process as well as the cluster-specific shared frailty distribution, along with an efficient sampling algorithm for posterior inference. Our method is applied to a recent cluster randomized trial on fall injury prevention.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"73 3","pages":"598-620"},"PeriodicalIF":1.0,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11271983/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141789690","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Multi-source longitudinal data have become increasingly common. This type of data refers to longitudinal datasets collected from multiple sources describing the same set of individuals. Representing distinct features of the individuals, each data source may consist of multiple longitudinal markers of distinct types and measurement frequencies. Motivated by the CHILD cohort study, we develop a model for joint clustering multi-source longitudinal data. The proposed model allows each data source to follow source-specific clustering, and they are aggregated to yield a global clustering. The proposed model is demonstrated through real-data analysis and simulation study.
{"title":"A Bayesian latent class model for integrating multi-source longitudinal data: application to the CHILD cohort study","authors":"Zihang Lu, Padmaja Subbarao, Wendy Lou","doi":"10.1093/jrsssc/qlad100","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad100","url":null,"abstract":"Abstract Multi-source longitudinal data have become increasingly common. This type of data refers to longitudinal datasets collected from multiple sources describing the same set of individuals. Representing distinct features of the individuals, each data source may consist of multiple longitudinal markers of distinct types and measurement frequencies. Motivated by the CHILD cohort study, we develop a model for joint clustering multi-source longitudinal data. The proposed model allows each data source to follow source-specific clustering, and they are aggregated to yield a global clustering. The proposed model is demonstrated through real-data analysis and simulation study.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"46 16","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136282378","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}
Julie Zhang, Gabriel A Preising, Molly Schumer, Julia A Palacios
Abstract An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.
{"title":"CRP-Tree: a phylogenetic association test for binary traits","authors":"Julie Zhang, Gabriel A Preising, Molly Schumer, Julia A Palacios","doi":"10.1093/jrsssc/qlad098","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad098","url":null,"abstract":"Abstract An important problem in evolutionary genomics is to investigate whether a certain trait measured on each sample is associated with the sample phylogenetic tree. The phylogenetic tree represents the shared evolutionary history of the samples and it is usually estimated from molecular sequence data at a locus or from other type of genetic data. We propose a model for trait evolution inspired by the Chinese Restaurant Process that includes a parameter that controls the degree of preferential attachment, that is, the tendency of nodes in the tree to subtend from nodes of the same type. This model with no preferential attachment is equivalent to a structured coalescent model with simultaneous migration and coalescence events and serves as a null model. We derive a test for phylogenetic binary trait association with linear computational complexity and empirically demonstrate that it is more powerful than some other methods. We apply our test to study the phylogenetic association of some traits in swordtail fish, breast cancer, yellow fever virus, and influenza A H1N1 virus. R-package implementation of our methods is available at https://github.com/jyzhang27/CRPTree.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"53 3","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"136281917","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}
Fedelis Mutiso, Hong Li, John L Pearce, Sara E Benjamin-Neelon, Noel T Mueller, Brian Neelon
Abstract The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a ‘vulnerability effect’ that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.
{"title":"Bayesian kernel machine regression for count data: modelling the association between social vulnerability and COVID-19 deaths in South Carolina","authors":"Fedelis Mutiso, Hong Li, John L Pearce, Sara E Benjamin-Neelon, Noel T Mueller, Brian Neelon","doi":"10.1093/jrsssc/qlad094","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad094","url":null,"abstract":"Abstract The COVID-19 pandemic created an unprecedented global health crisis. Recent studies suggest that socially vulnerable communities were disproportionately impacted, although findings are mixed. To quantify social vulnerability in the US, many studies rely on the Social Vulnerability Index (SVI), a county-level measure comprising 15 census variables. Typically, the SVI is modelled in an additive manner, which may obscure non-linear or interactive associations, further contributing to inconsistent findings. As a more robust alternative, we propose a negative binomial Bayesian kernel machine regression (BKMR) model to investigate dynamic associations between social vulnerability and COVID-19 death rates, thus extending BKMR to the count data setting. The model produces a ‘vulnerability effect’ that quantifies the impact of vulnerability on COVID-19 death rates in each county. The method can also identify the relative importance of various SVI variables and make future predictions as county vulnerability profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates spatial effects, county-level covariates, and smooth temporal functions. For Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We conduct a simulation study to highlight the approach and apply the method to a study of COVID-19 deaths in the US state of South Carolina during the 2021 calendar year.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"25 4","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135874680","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}
Fiona E Turner, Caitlin E Buck, Julie M Jones, Louise C Sime, Irene Malmierca Vallet, Richard D Wilkinson
Abstract The Antarctic ice sheet (AIS) is the Earth’s largest store of frozen water; understanding how it changed in the past allows us to improve projections of how it, and sea levels, may change. Here, we use previous AIS reconstructions, water isotope ratios from ice cores, and simulator predictions of the relationship between the ice-sheet shape and isotope ratios to create a model of the AIS at the Last Glacial Maximum. We develop a prior distribution that captures expert opinion about the AIS, generate a designed ensemble of potential shapes, run these through the climate model HadCM3, and train a Gaussian process emulator of the link between ice-sheet shape and isotope ratios. To make the analysis computationally tractable, we develop a preferential principal component method that allows us to reduce the dimension of the problem in a way that accounts for the differing importance we place in reconstructions, allowing us to create a basis that reflects prior uncertainty. We use Markov chain Monte Carlo to sample from the posterior distribution, finding shapes for which HadCM3 predicts isotope ratios closely matching observations from ice cores. The posterior distribution allows us to quantify the uncertainty in the reconstructed shape, a feature missing in other analyses.
{"title":"Reconstructing the Antarctic ice-sheet shape at the Last Glacial Maximum using ice-core data","authors":"Fiona E Turner, Caitlin E Buck, Julie M Jones, Louise C Sime, Irene Malmierca Vallet, Richard D Wilkinson","doi":"10.1093/jrsssc/qlad078","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad078","url":null,"abstract":"Abstract The Antarctic ice sheet (AIS) is the Earth’s largest store of frozen water; understanding how it changed in the past allows us to improve projections of how it, and sea levels, may change. Here, we use previous AIS reconstructions, water isotope ratios from ice cores, and simulator predictions of the relationship between the ice-sheet shape and isotope ratios to create a model of the AIS at the Last Glacial Maximum. We develop a prior distribution that captures expert opinion about the AIS, generate a designed ensemble of potential shapes, run these through the climate model HadCM3, and train a Gaussian process emulator of the link between ice-sheet shape and isotope ratios. To make the analysis computationally tractable, we develop a preferential principal component method that allows us to reduce the dimension of the problem in a way that accounts for the differing importance we place in reconstructions, allowing us to create a basis that reflects prior uncertainty. We use Markov chain Monte Carlo to sample from the posterior distribution, finding shapes for which HadCM3 predicts isotope ratios closely matching observations from ice cores. The posterior distribution allows us to quantify the uncertainty in the reconstructed shape, a feature missing in other analyses.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135154123","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}
Jiachen Zhang, Matthew Bonas, Diogo Bolster, Geir-Arne Fuglstad, S. Castruccio
Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a non-stationary, sparse process such as precipitation at very high spatial resolution requires the interpolation of global datasets at the location where ground stations are available with statistical models able to capture complex non-Gaussian global space–time dependence structures. In this work, we propose a new approach based on capturing the spatially varying anisotropy of a latent Gaussian process via a locally deformed stochastic partial differential equation (SPDE) with a buffer allowing for a different spatial structure across land and sea. The finite volume approximation of the SPDE, coupled with integrated nested Laplace approximation ensures feasible Bayesian inference for tens of millions of observations. The simulation studies showcase the improved predictability of the proposed approach against stationary and no-buffer alternatives. The proposed approach is then used to yield high-resolution simulations of daily precipitation across the United States.
{"title":"High-resolution global precipitation downscaling with latent Gaussian models and non-stationary stochastic partial differential equation structure","authors":"Jiachen Zhang, Matthew Bonas, Diogo Bolster, Geir-Arne Fuglstad, S. Castruccio","doi":"10.1093/jrsssc/qlad084","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad084","url":null,"abstract":"\u0000 Obtaining high-resolution maps of precipitation data can provide key insights to stakeholders to assess a sustainable access to water resources at urban scale. Mapping a non-stationary, sparse process such as precipitation at very high spatial resolution requires the interpolation of global datasets at the location where ground stations are available with statistical models able to capture complex non-Gaussian global space–time dependence structures. In this work, we propose a new approach based on capturing the spatially varying anisotropy of a latent Gaussian process via a locally deformed stochastic partial differential equation (SPDE) with a buffer allowing for a different spatial structure across land and sea. The finite volume approximation of the SPDE, coupled with integrated nested Laplace approximation ensures feasible Bayesian inference for tens of millions of observations. The simulation studies showcase the improved predictability of the proposed approach against stationary and no-buffer alternatives. The proposed approach is then used to yield high-resolution simulations of daily precipitation across the United States.","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"29 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-09-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86900064","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}
Claudio Heinrich‐Mertsching, J. C. Wahl, A. Ordoñez, M. Stien, John Elvsborg, O. Haug, T. Thorarinsdottir
{"title":"Assessing present and future risk of water damage using. Response to Comments","authors":"Claudio Heinrich‐Mertsching, J. C. Wahl, A. Ordoñez, M. Stien, John Elvsborg, O. Haug, T. Thorarinsdottir","doi":"10.1093/jrsssc/qlad067","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad067","url":null,"abstract":"","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"26 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83202519","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}
{"title":"Ankur Dutta’s contribution to the Discussion of “The First Discussion Meeting on Statistical aspects of climate change”","authors":"Ankur Dutta","doi":"10.1093/jrsssc/qlad052","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad052","url":null,"abstract":"","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"7 1","pages":""},"PeriodicalIF":1.6,"publicationDate":"2023-08-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82465798","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}
Austin Talbot, David Dunson, Kafui Dzirasa, David Carlson
Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of latent networks, where the latent variables predict a relevant outcome. We use supervised autoencoders (SAEs) to improve predictive performance in this context, describe the conditions where SAEs improve predictions, and provide modelling constraints to ensure biological relevance. We experimentally validate our approach by finding a network associated with stress that aligns with a previous stimulation protocol and characterizing a genotype associated with bipolar disorder.
{"title":"Estimating a brain network predictive of stress and genotype with supervised autoencoders.","authors":"Austin Talbot, David Dunson, Kafui Dzirasa, David Carlson","doi":"10.1093/jrsssc/qlad035","DOIUrl":"https://doi.org/10.1093/jrsssc/qlad035","url":null,"abstract":"<p><p>Targeted brain stimulation has the potential to treat mental illnesses. We develop an approach to help design protocols by identifying relevant multi-region electrical dynamics. Our approach models these dynamics as a superposition of latent networks, where the latent variables predict a relevant outcome. We use supervised autoencoders (SAEs) to improve predictive performance in this context, describe the conditions where SAEs improve predictions, and provide modelling constraints to ensure biological relevance. We experimentally validate our approach by finding a network associated with stress that aligns with a previous stimulation protocol and characterizing a genotype associated with bipolar disorder.</p>","PeriodicalId":49981,"journal":{"name":"Journal of the Royal Statistical Society Series C-Applied Statistics","volume":"72 4","pages":"912-936"},"PeriodicalIF":1.6,"publicationDate":"2023-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10474874/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"10163295","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"数学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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