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Rachael Hageman Blair

Showing results (1-10 of 34) with videos related to

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BMC Medical Research Methodology|December 10, 2016
A comparative study: classification vs. user-based collaborative filtering for clinical predictionFang Hao, Rachael Hageman Blair
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|January 19, 2016
A FRAMEWORK FOR ATTRIBUTE-BASED COMMUNITY DETECTION WITH APPLICATIONS TO INTEGRATED FUNCTIONAL GENOMICSHan Yu, Rachael Hageman Blair
Journal of Applied Statistics|June 16, 2022
Scalable module detection for attributed networks with applications to breast cancerHan Yu, Rachael Hageman Blair
BMC Bioinformatics|July 12, 2019
Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's diseaseHan Yu, Rachael Hageman Blair
Wiley Interdisciplinary Reviews. Computational Statistics|December 30, 2022
Stability estimation for unsupervised clustering: A reviewTianmou Liu, Han Yu, Rachael Hageman Blair
Statistical Applications in Genetics and Molecular Biology|November 21, 2023
Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritizationTiange Shi, Han Yu, Rachael Hageman Blair
Metabolic Engineering|November 7, 2022
Machine-learning guided elucidation of contribution of individual steps in the mevalonate pathway and construction of a yeast platform strain for terpenoid productionMinakshi Mukherjee, Rachael Hageman Blair, Zhen Q Wang
Gene|July 25, 2017
Investigation of the role of DNA methylation in the expression of ERBB2 in human myocardiumAdolfo Quiñones-Lombraña, Rachael Hageman Blair, Javier G Blanco
Plos Computational Biology|April 13, 2012
What can causal networks tell us about metabolic pathways?Rachael Hageman Blair, Daniel J Kliebenstein, Gary A Churchill
Journal of Pharmacokinetics and Pharmacodynamics|October 6, 2021
Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progressionMason McComb, Rachael Hageman Blair, Martin Lysy, et al.
Pageof 4

Showing results (1-10 of 34) with videos related to

Sort By:
Pageof 4
BMC Medical Research Methodology|December 10, 2016
A comparative study: classification vs. user-based collaborative filtering for clinical predictionFang Hao, Rachael Hageman Blair
Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing|January 19, 2016
A FRAMEWORK FOR ATTRIBUTE-BASED COMMUNITY DETECTION WITH APPLICATIONS TO INTEGRATED FUNCTIONAL GENOMICSHan Yu, Rachael Hageman Blair
Journal of Applied Statistics|June 16, 2022
Scalable module detection for attributed networks with applications to breast cancerHan Yu, Rachael Hageman Blair
BMC Bioinformatics|July 12, 2019
Integration of probabilistic regulatory networks into constraint-based models of metabolism with applications to Alzheimer's diseaseHan Yu, Rachael Hageman Blair
Wiley Interdisciplinary Reviews. Computational Statistics|December 30, 2022
Stability estimation for unsupervised clustering: A reviewTianmou Liu, Han Yu, Rachael Hageman Blair
Statistical Applications in Genetics and Molecular Biology|November 21, 2023
Integrated regulatory and metabolic networks of the tumor microenvironment for therapeutic target prioritizationTiange Shi, Han Yu, Rachael Hageman Blair
Metabolic Engineering|November 7, 2022
Machine-learning guided elucidation of contribution of individual steps in the mevalonate pathway and construction of a yeast platform strain for terpenoid productionMinakshi Mukherjee, Rachael Hageman Blair, Zhen Q Wang
Gene|July 25, 2017
Investigation of the role of DNA methylation in the expression of ERBB2 in human myocardiumAdolfo Quiñones-Lombraña, Rachael Hageman Blair, Javier G Blanco
Plos Computational Biology|April 13, 2012
What can causal networks tell us about metabolic pathways?Rachael Hageman Blair, Daniel J Kliebenstein, Gary A Churchill
Journal of Pharmacokinetics and Pharmacodynamics|October 6, 2021
Machine learning-guided, big data-enabled, biomarker-based systems pharmacology: modeling the stochasticity of natural history and disease progressionMason McComb, Rachael Hageman Blair, Martin Lysy, et al.
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