Data science in science最新文献

{"title":"Neurodatascience: Past, Present, and Future.","authors":"Keiland W Cooper, Babak Shahbaba, Norbert J Fortin","doi":"10.1080/26941899.2026.2619222","DOIUrl":"10.1080/26941899.2026.2619222","url":null,"abstract":"<p><p>The study of the brain is a compelling example of the power of convergent science. Over the last few decades, advances in neuroscience techniques and experimentation, as well as in data science tools to analyze the resulting data, have dramatically furthered our understanding of fundamental brain functions. Historically, it has been common for analytical approaches to have a considerable lag in development following the availability of new neuroscience techniques. However, this relationship has not simply been unidirectional, as there have been examples in which analytical developments have directly led to new scientific questions and experiments. Here we review how this interplay between neuroscience and data science advances has unfolded in the past and into the present, with a focus on electrophysiology and calcium imaging. Applying lessons learned from the past and present, we then discuss expected developments, challenges, and opportunities in the future. We end by providing recommendations on how to foster the necessary team science approach to continue the advancement of research at the intersection of neuroscience and data science, which we call neurodatascience, toward a sustainable future.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13218777/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148139913","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}

{"title":"A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development Study.","authors":"Aidan Neher, Apostolos Stamenos, Mark Fiecas, Sandra E Safo, Thierry Chekouo","doi":"10.1080/26941899.2025.2600125","DOIUrl":"10.1080/26941899.2025.2600125","url":null,"abstract":"<p><p>Integrating high-dimensional, heterogeneous data from multi-site cohort studies with complex hierarchical structures poses significant variable selection and prediction challenges. We extend the Bayesian Integrative Analysis and Prediction (BIP) framework to enable simultaneous variable selection and outcome modeling in data of a multi-view nested hierarchical structure. We apply the proposed Bayesian Integrative Mixed Modeling (BIPmixed) framework to the Adolescent Brain Cognitive Development (ABCD) Study, leveraging multi-view data, including structural and functional MRI and early life adversity (ELA) metrics, to identify relevant variables and predict the behavioral outcome. BIPmixed incorporates 2-level nested random effects to enhance interpretability and make predictions in hierarchical data settings. Simulation studies illustrate BIPmixed's robustness in distinct random effect settings, highlighting its use for complex study designs. Our findings suggest that BIPmixed effectively integrates multi-view data while accounting for nested sampling, making it a valuable tool for analyzing large-scale studies with hierarchical data.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12811013/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145999117","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}

{"title":"Evaluating Missing Data Imputation Strategies for Environmental Mixture Models: A Simulation Study and Applied Analysis.","authors":"Yvonne S Boafo, Sayed Mostafa, Emmanuel Obeng-Gyasi","doi":"10.1080/26941899.2026.2696661","DOIUrl":"10.1080/26941899.2026.2696661","url":null,"abstract":"<p><p>Missing data are common in environmental mixture studies and can bias inference if not properly addressed. This study evaluates how different imputation strategies influence variable selection performance in six mixture modeling frameworks: Weighted Quantile Sum (WQS), Bayesian WQS (BWQS), Quantile g-computation (Q-gcomp), Bayesian Kernel Machine Regression (BKMR), Elastic Net, and Least Absolute Shrinkage and Selection Operator (LASSO). Using Monte Carlo simulations (MCS), we generated multivariate normal (MVNORM) and multivariate t- (MVT) distributed exposures under linear and nonlinear outcome structures. Missingness was introduced at 25% for exposure and outcome variables, and at 5%-25% under both missing-at-random (MAR) and missing-not-at-random (MNAR) mechanisms. The study compares single imputation (SI) methods (mean, median, and k-nearest neighbors [KNN]) with multiple imputation approaches [MI] (MICE and Amelia) based on sensitivity (SE), specificity (SP), and false discovery rate (FDR). Across simulations, MI consistently improved variable selection performance under MAR, whereas SI and listwise deletion increased variability and reduced accuracy. Under MNAR, performance declined across all methods, with greater instability observed in flexible models such as BKMR and WQS. Q-gcomp demonstrated the most consistent balance across SE, SP, and FDR and remained relatively robust to violations of the MAR assumption. Additional analyses highlight the importance of imputation model specification: excluding relevant covariates (e.g. income) from the imputation process increased variability and reduced stability, particularly for flexible models and binary outcomes. Application to NHANES 2007-2014 data (<i>n</i> = 8,233) showed that MI improved the stability of variable importance estimates, with BWQS and Q-gcomp yielding more reproducible exposure rankings. Overall, results demonstrate that both the missingness mechanism and the choice and specification of imputation strategy substantially influence variable selection in mixture models, underscoring the importance of carefully designed imputation procedures in environmental epidemiology.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"5 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2026-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13449937/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148698484","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}

{"title":"Bayesian Covariate-Dependent Circadian Modeling of Rest-Activity Rhythms.","authors":"Beniamino Hadj-Amar, Vaishnav Krishnan, Marina Vannucci","doi":"10.1080/26941899.2025.2474943","DOIUrl":"10.1080/26941899.2025.2474943","url":null,"abstract":"<p><p>We propose a Bayesian covariate-dependent anti-logistic circadian model for analyzing activity data collected <i>via</i> wrist-worn wearable devices. The proposed approach integrates covariates into the modeling of the amplitude and phase parameters, facilitating cohort-level analysis with enhanced flexibility and interpretability. To promote model sparsity, we employ an <i>l</i> ₁-ball projection prior, enabling precise control over complexity while identifying significant predictors. We assess performances on simulated data and then apply the method to real-world actigraphy data from people with epilepsy. Our results demonstrate the model's effectiveness in uncovering complex relationships among demographic, psychological, and medical factors influencing rest-activity rhythms, offering insights for personalized clinical assessments and healthcare interventions.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13327678/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148378280","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}

{"title":"A Bayesian Time-Varying Psychophysiological Interaction Model.","authors":"Brian Schetzsle, Jaylen Lee, Aaron Bornstein, Babak Shahbaba, Michele Guindani","doi":"10.1080/26941899.2025.2519436","DOIUrl":"10.1080/26941899.2025.2519436","url":null,"abstract":"<p><p>Functional connectivity, the study of coordination between distinct brain regions, is a key focus in neuroscience. The Psychophysiological Interaction (PPI) model, commonly used to infer task-dependent functional connectivity, is limited by its susceptibility to confounding effects. We propose using partial correlations, instead of PPI regression coefficients, as they correct for confounding. We show how the PPI model can be used to estimate the precision matrix of a Gaussian Graphical Model (GGM), from which partial correlations are easily derived. We then propose a Bayesian extension to the PPI model that allows this measure of functional connectivity to vary over time. We enforce sparsity in the GGM precision matrix through scale-mixture shrinkage priors, mitigating overfitting. Additionally, we identify structural zeros in the precision matrix using a Bayesian multicomparison decision-theoretic framework. We demonstrate the efficacy of our model over the standard PPI model using simulated data and we further apply it to human fMRI data from a serial reaction time experiment. Our framework offers a more robust and dynamic approach to functional connectivity analysis.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13251719/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148229311","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}

{"title":"Enhancing Health Research with Machine Learning: Practical Case Studies Using the <i>All of Us</i> Researcher Workbench.","authors":"Jonathan R Holt, Stefanee Tillman, Javan Carter, Edward Preble, Sheryl C Cates, Daniel Brannock, Michael Long, John McCarthy, Leslie Zapata Leiva, Jamboor K Vishwanatha, Toufeeq Syed, Legand Burge, Robert T Mallet, Shelly Kowalczyk, Jennifer D Uhrig, Megan A Lewis","doi":"10.1080/26941899.2025.2523871","DOIUrl":"10.1080/26941899.2025.2523871","url":null,"abstract":"<p><p>Machine learning is revolutionizing health research by enabling scalable analysis across complex datasets. The <i>All of Us</i> Research Program offers unprecedented access to a wealth of health data. To harness this potential, researchers must navigate the <i>All of Us</i> database structure, develop machine learning skills, and apply coding effectively. This paper presents case studies designed to impart these skills using the <i>All of Us</i> Researcher Workbench. Our case studies cover critical topics, such as dataset selection, data cleaning, machine learning applications, and visualization in Python, which together provide the foundation of a targeted training program. Evaluated through pre- and post-program surveys, the program significantly boosted participants' machine learning competencies. By detailing our approach and findings, we aim to guide researchers in harnessing the full potential of the <i>All of Us</i> dataset, thereby advancing precision medicine.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"4 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2025-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12362663/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144980907","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}

{"title":"Biclustering Multivariate Longitudinal Data with Application to Recovery Trajectories of White Matter After Sport-Related Concussion","authors":"Caleb Weaver, Luo Xiao, Qiuting Wen, Yu-Chien Wu, Jaroslaw Harezlak","doi":"10.1080/26941899.2024.2376535","DOIUrl":"https://doi.org/10.1080/26941899.2024.2376535","url":null,"abstract":"","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":" 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-07-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141832378","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}

{"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":"<p><p>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.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"3 1","pages":""},"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}

{"title":"Assessment of Glioblastoma Multiforme Tumor Heterogeneity via MRI-derived Shape and Intensity Features.","authors":"Yi Tang Chen, Sebastian Kurtek","doi":"10.1080/26941899.2024.2415690","DOIUrl":"10.1080/26941899.2024.2415690","url":null,"abstract":"<p><p>We use a geometric approach to jointly characterize tumor shape and intensity along the tumor contour, as captured in magnetic resonance images, in the context of glioblastoma multiforme. Key properties of the proposed shape+intensity representation include invariance to translation, scale, rotation and reparameterization, which enable objective characterization and comparison of these crucial tumor features. The representation further allows the user to tune the emphasis of the shape and intensity components during registration, comparison and statistical summarization (averaging, computation of overall variance and exploration of variability via principal component analysis). In addition, we define a composite distance that is able to integrate shape and intensity information from two imaging modalities. The proposed framework can be integrated with distance-based clustering for the purpose of discovering groups of subjects with distinct survival prognosis. When applied to a cohort of subjects with glioblastoma multiforme, we discover groups with large median survival differences. We further tie the subjects' cluster memberships to tumor heterogeneity. Our results suggest that tumor shape variation plays an important role in disease prognosis.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12124832/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144200928","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}

{"title":"TIME-VARYING <i>ℓ</i> ₀ OPTIMIZATION FOR SPIKE INFERENCE FROM MULTI-TRIAL CALCIUM RECORDINGS.","authors":"Tong Shen, Mingyu DU, Kevin Johnston, Gyorgy Lur, Xiangmin Xu, Hernando Ombao, Michele Guindani, Zhaoxia Yu","doi":"10.1080/26941899.2024.2407770","DOIUrl":"10.1080/26941899.2024.2407770","url":null,"abstract":"<p><p>Optical imaging of genetically encoded calcium indicators is a powerful tool to record the activity of a large number of neurons simultaneously over a long period of time from freely behaving animals. However, determining the exact time at which a neuron spikes and estimating the underlying firing rate from calcium fluorescence data remains challenging, especially for calcium imaging data obtained from a longitudinal study. We propose a multi-trial time-varying <math> <mrow><msub><mo>ℓ</mo> <mn>0</mn></msub> </mrow> </math> penalized method to jointly detect spikes and estimate firing rates by robustly integrating evolving neural dynamics across trials. Our simulation study shows that the proposed method performs well in both spike detection and firing rate estimation. We demonstrate the usefulness of our method on calcium fluorescence trace data from two studies, with the first study showing differential firing rate functions between two behaviors and the second study showing evolving firing rate functions across trials due to learning.</p>","PeriodicalId":72770,"journal":{"name":"Data science in science","volume":"3 1","pages":""},"PeriodicalIF":0.0,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12316062/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144777050","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}