Statistics in Biosciences最新文献_第3页

Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression. 综合主成分回归预测泛癌症药物反应。

IF 0.5

Statistics in Biosciences Pub Date : 2024-08-18 DOI: 10.1007/s12561-024-09451-8

Qingzhi Liu, Gen Li, Veerabhadran Baladandayuthapani

{"title":"Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression.","authors":"Qingzhi Liu, Gen Li, Veerabhadran Baladandayuthapani","doi":"10.1007/s12561-024-09451-8","DOIUrl":"10.1007/s12561-024-09451-8","url":null,"abstract":"The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2024-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13225719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148151557","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

Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data. 基于不准确电子病历数据的联邦半监督迁移学习增强遗传风险预测。

IF 0.4

Statistics in Biosciences Pub Date : 2024-08-13 DOI: 10.1007/s12561-024-09449-2

Yuying Lu, Tian Gu, Rui Duan

{"title":"Enhancing Genetic Risk Prediction through Federated Semi-Supervised Transfer Learning with Inaccurate Electronic Health Record Data.","authors":"Yuying Lu, Tian Gu, Rui Duan","doi":"10.1007/s12561-024-09449-2","DOIUrl":"10.1007/s12561-024-09449-2","url":null,"abstract":"Large-scale genomics data combined with Electronic Health Records (EHRs) illuminate the path towards personalized disease management and enhanced medical interventions. However, the absence of \"gold standard\" disease labels makes the development of machine learning models a challenging task. Additionally, imbalances in demographic representation within datasets compromise the development of unbiased healthcare solutions. In response to these challenges, we introduce FEderated Semi-Supervised Transfer Learning (FEST) for improving disease risk predictions in underrepresented populations. FEST facilitates the collaborative training of models across various institutions by leveraging both labeled and unlabeled data from diverse subpopulations. It addresses distributional variations across different populations and healthcare institutions by combining density ratio reweighting and model calibration techniques. Federated learning algorithms are developed for training models using only summary-level statistics. We perform simulation studies to assess the efficacy of FEST in comparisons with a few alternative methods. Subsequently, we apply FEST to training a genetic risk prediction model for type 2 diabetes that targets the African-Ancestry population using data from the Massachusetts General Brigham (MGB) Biobank. Both our computational experiments and real-world data application underline the superior performance of FEST over competing methods.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.4,"publicationDate":"2024-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12409711/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145013364","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

Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information. 带噪声网络信息的多模态数据综合分析的图导贝叶斯因子模型。

IF 0.5

Statistics in Biosciences Pub Date : 2024-08-11 DOI: 10.1007/s12561-024-09452-7

Wenrui Li, Qiyiwen Zhang, Kewen Qu, Qi Long

{"title":"Graph-guided Bayesian Factor Model for Integrative Analysis of Multi-modal Data with Noisy Network Information.","authors":"Wenrui Li, Qiyiwen Zhang, Kewen Qu, Qi Long","doi":"10.1007/s12561-024-09452-7","DOIUrl":"10.1007/s12561-024-09452-7","url":null,"abstract":"There is a growing body of literature on factor analysis that can capture individual and shared structures in multi-modal data. However, few of these approaches incorporate biological knowledge such as functional genomics and functional metabolomics. Graph-guided statistical learning methods that can incorporate knowledge of underlying networks have been shown to improve predication and classification accuracy, and yield more interpretable results. Moreover, these methods typically use graphs extracted from existing databases or rely on subject matter expertise which are known to be incomplete and may contain false edges. To address this gap, we propose a graph-guided Bayesian factor model that can account for network noise and identify globally shared, partially shared and modality-specific latent factors in multimodal data. Specifically, we use two sources of network information, including the noisy graph extracted from existing databases and the estimated graph from observed features in the dataset at hand, to inform the model for the true underlying network via a latent scale modeling framework. This model is coupled with the Bayesian factor analysis model with shrinkage priors to encourage feature-wise and modal-wise sparsity, thereby allowing feature selection and identification of factors of each type. We develop an efficient Markov chain Monte Carlo algorithm for posterior sampling. We demonstrate the advantages of our method over existing methods in simulations, and through analyses of gene expression and metabolomics datasets for Alzheimer's disease.","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":" ","pages":""},"PeriodicalIF":0.5,"publicationDate":"2024-08-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12221265/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144691908","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

Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies. 纵向过程几何特征和嵌套时间尺度上的离散生存时间联合建模:在繁殖力研究中的应用

IF 0.5

Statistics in Biosciences Pub Date : 2024-04-01 Epub Date: 2023-08-11 DOI: 10.1007/s12561-023-09381-x

Abhisek Saha, Ling Ma, Animikh Biswas, Rajeshwari Sundaram

{"title":"Joint Modeling of Geometric Features of Longitudinal Process and Discrete Survival Time Measured on Nested Timescales: An Application to Fecundity Studies.","authors":"Abhisek Saha, Ling Ma, Animikh Biswas, Rajeshwari Sundaram","doi":"10.1007/s12561-023-09381-x","DOIUrl":"10.1007/s12561-023-09381-x","url":null,"abstract":"In biomedical studies, longitudinal processes are collected till time-to-event, sometimes on nested timescales (example, days within months). Most of the literature in joint modeling of longitudinal and time-to-event data has focused on modeling the mean or dispersion of the longitudinal process with the hazard for time-to-event. However, based on the motivating studies, it may be of interest to investigate how the cycle-level geometric features (such as the curvature, location and height of a peak), of a cyclical longitudinal process is associated with the time-to-event being studied. We propose a shared parameter joint model for a cyclical longitudinal process and a discrete survival time, measured on nested timescales, where the cycle-varying geometric feature is modeled through a linear mixed effects model and a proportional hazards model for the discrete survival time. The proposed approach allows for prediction of survival probabilities for future subjects based on their available longitudinal measurements. Our proposed model and approach is illustrated through simulation and analysis of Stress and Time-to-Pregnancy, a component of Oxford Conception Study. A joint modeling approach was used to assess whether the cycle-specific geometric features of the lutenizing hormone measurements, such as its peak or its curvature, are associated with time-to-pregnancy (TTP).","PeriodicalId":45094,"journal":{"name":"Statistics in Biosciences","volume":"1 1","pages":"86-106"},"PeriodicalIF":0.5,"publicationDate":"2024-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12687766/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46399657","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

Use of Real-World EMR Data to Rapidly Evaluate Treatment Effects of Existing Drugs for Emerging Infectious Diseases: Remdesivir for COVID-19 Treatment as an Example 利用真实世界的 EMR 数据快速评估现有药物对新发传染病的治疗效果：以治疗 COVID-19 的雷米地韦为例

IF 1

Statistics in Biosciences Pub Date : 2024-01-02 DOI: 10.1007/s12561-023-09411-8

Chenguang Zhang, Masayuki Nigo, Shivani Patel, Duo Yu, Edward Septimus, Hulin Wu

引用次数: 0

Modeling Historic Arsenic Exposures and Spatial Risk for Bladder Cancer 砷历史暴露与膀胱癌空间风险建模

IF 1

Statistics in Biosciences Pub Date : 2023-12-17 DOI: 10.1007/s12561-023-09404-7

Joseph Boyle, Mary H. Ward, Stella Koutros, M. Karagas, M. Schwenn, Alison T. Johnson, Debra T. Silverman, David C. Wheeler

引用次数: 0

Considerations and Targeted Approaches to Identifying Bad Actors in Exposure Mixtures 识别暴露混合物中不良作用因子的考虑因素和有针对性的方法

IF 1

Statistics in Biosciences Pub Date : 2023-12-12 DOI: 10.1007/s12561-023-09409-2

Alexander P. Keil, Katie M. O’Brien

引用次数: 0

Causal Mediation Tree Model for Feature Identification on High-Dimensional Mediators 用于识别高维中介特征的因果中介树模型

IF 1

Statistics in Biosciences Pub Date : 2023-12-02 DOI: 10.1007/s12561-023-09402-9

Yao Li, Wei Xu

引用次数: 0

Evaluating Effects of Various Exposures on Mortality Risk of Opioid Use Disorders with Linked Administrative Databases 利用关联行政数据库评估各种暴露对阿片类药物使用障碍死亡率风险的影响

IF 1

Statistics in Biosciences Pub Date : 2023-12-01 DOI: 10.1007/s12561-023-09407-4

Trevor J. Thomson, X. Joan Hu, Bohdan Nosyk

引用次数: 0

Use of Generalized Propensity Scores for Assessing Effects of Multiple Exposures 使用广义倾向分数评估多重暴露的影响

IF 1

Statistics in Biosciences Pub Date : 2023-12-01 DOI: 10.1007/s12561-023-09403-8

Kecheng Li, Tugba Akkaya Hocagil, Richard J. Cook, Louise M. Ryan, R. C. Carter, Khue-Dung Dang, J. Jacobson, S. Jacobson

引用次数: 0