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Lisa Uechi

Showing results (1-10 of 9) with videos related to

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Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|November 30, 2018
Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability FunctionLisa Uechi, David J Galas, Nikita A Sakhanenko
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|January 4, 2021
Toward an Information Theory of Quantitative GeneticsDavid J Galas, James Kunert-Graf, Lisa Uechi, et al.
NPJ Systems Biology and Applications|March 26, 2024
Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemiaLisa Uechi, Swetha Vasudevan, Daniela Vilenski, et al.
Plos One|December 3, 2020
Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanismsLisa Uechi, Mahjoubeh Jalali, Jayson D Wilbur, et al.
Science Advances|April 22, 2022
Dynamic patterns of microRNA expression during acute myeloid leukemia state-transitionDavid E Frankhouser, Denis O'Meally, Sergio Branciamore, et al.
Annals of Clinical and Translational Neurology|August 30, 2023
Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progressionLucas Vu, Krystine Garcia-Mansfield, Antonio Pompeiano, et al.
Biorxiv : the Preprint Server for Biology|June 4, 2026
Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid LeukemiaJennifer Rangel Ambriz, Ziang Chen, Yu-Hsuan Fu, et al.
Biorxiv : the Preprint Server for Biology|October 24, 2023
State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid LeukemiaDavid E Frankhouser, Russell C Rockne, Lisa Uechi, et al.
Leukemia|February 2, 2024
State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemiaDavid E Frankhouser, Russell C Rockne, Lisa Uechi, et al.
Pageof 1

Showing results (1-10 of 9) with videos related to

Sort By:
Pageof 1
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|November 30, 2018
Multivariate Analysis of Data Sets with Missing Values: An Information Theory-Based Reliability FunctionLisa Uechi, David J Galas, Nikita A Sakhanenko
Journal of Computational Biology : a Journal of Computational Molecular Cell Biology|January 4, 2021
Toward an Information Theory of Quantitative GeneticsDavid J Galas, James Kunert-Graf, Lisa Uechi, et al.
NPJ Systems Biology and Applications|March 26, 2024
Transcriptome free energy can serve as a dynamic patient-specific biomarker in acute myeloid leukemiaLisa Uechi, Swetha Vasudevan, Daniela Vilenski, et al.
Plos One|December 3, 2020
Complex genetic dependencies among growth and neurological phenotypes in healthy children: Towards deciphering developmental mechanismsLisa Uechi, Mahjoubeh Jalali, Jayson D Wilbur, et al.
Science Advances|April 22, 2022
Dynamic patterns of microRNA expression during acute myeloid leukemia state-transitionDavid E Frankhouser, Denis O'Meally, Sergio Branciamore, et al.
Annals of Clinical and Translational Neurology|August 30, 2023
Proteomics and mathematical modeling of longitudinal CSF differentiates fast versus slow ALS progressionLucas Vu, Krystine Garcia-Mansfield, Antonio Pompeiano, et al.
Biorxiv : the Preprint Server for Biology|June 4, 2026
Multiomic State-Transitions Reveal Post-Treatment Transcriptome Desynchronization in Acute Myeloid LeukemiaJennifer Rangel Ambriz, Ziang Chen, Yu-Hsuan Fu, et al.
Biorxiv : the Preprint Server for Biology|October 24, 2023
State-transition Modeling of Blood Transcriptome Predicts Disease Evolution and Treatment Response in Chronic Myeloid LeukemiaDavid E Frankhouser, Russell C Rockne, Lisa Uechi, et al.
Leukemia|February 2, 2024
State-transition modeling of blood transcriptome predicts disease evolution and treatment response in chronic myeloid leukemiaDavid E Frankhouser, Russell C Rockne, Lisa Uechi, et al.
Pageof 1