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Data Science in Science|January 19, 2026
A Bayesian Integrative Mixed Modeling Framework for Analysis of the Multi-Site Adolescent Brain and Cognitive Development StudyAidan Neher, Apostolos Stamenos, Mark Fiecas, et al.
Biometrics|September 27, 2016
A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case studyThierry Chekouo, Francesco C Stingo, James D Doecke, et al.
Biometrics|February 3, 2015
miRNA-target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancerThierry Chekouo, Francesco C Stingo, James D Doecke, et al.
Cerebral Cortex (New York, N.Y. : 1991)|May 2, 2024
Quantitative proteomics of dorsolateral prefrontal cortex reveals an early pattern of synaptic dysmaturation in children with idiopathic autismS Hossein Fatemi, Arthur Eschenlauer, Justin Aman, et al.
BMJ Open|November 12, 2021
Integrating expert knowledge for dementia risk prediction in individuals with mild cognitive impairment (MCI): a study protocolMeng Wang, Eric E Smith, Nils Daniel Forkert, et al.
Oncology Letters|September 21, 2020
Y-box binding protein 1 acts as a negative regulator of stearoyl CoA desaturase 1 in clear cell renal cell carcinomaEric Jeffords, Samuel Freeman, Breanna Cole, et al.
BMC Medical Research Methodology|November 3, 2022
Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning modelsMeng Wang, Matthew Greenberg, Nils D Forkert, et al.
Scientific Reports|August 5, 2022
Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewaterXiaotian Dai, David Champredon, Aamir Fazil, et al.
Cancers|July 29, 2023
Development and External Validation of Partial Proportional Odds Risk Prediction Models for Cancer Stage at Diagnosis among Males and Females in CanadaTimofei Biziaev, Michelle L Aktary, Qinggang Wang, et al.
Statistical Methods in Medical Research|June 8, 2019
Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizesThierry Chekouo, Francesco C Stingo, Caleb A Class, et al.
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