Renee Brady
@ReneeBradyPhD
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Joined November 2017
Calling all high schoolers! Now accepting applications for 2025’s High School Internship Program in Integrated Mathematical Oncology. Please share with motivated aspiring scientists! Stipend included! Due 2/1/25. More info here: @MoffittNews @mathonco.
moffitt.org
HIP IMO (High-school student Internship Program in Integrated Mathematical Oncology ) provides science project mentorship as part of the high school’s curriculum consisting of 10 weeks of intensive...
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RT @ara_anderson: Todays @mathonco research in progress is from @ReneeBradyPhD lab member Daniel Glazar: Using PRO dynamics to predict pati….
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Just a reminder that applications for the High School Internship Program in Integrated Mathematical Oncology (HIP IMO) are due February 1st! . An awesome research opportunity for high schoolers. Stipend included!. More info here: @mathonco @MoffittNews.
moffitt.org
HIP IMO (High-school student Internship Program in Integrated Mathematical Oncology ) provides science project mentorship as part of the high school’s curriculum consisting of 10 weeks of intensive...
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RT @GreggNeuroLab: A very kind press release on efforts to improve cancer care. Huge thanks to my heros: @ara_anderson, @DanaAtayaMD, @Rene….
healthcare.utah.edu
Breast cancer patient Christopher Gregg is working to find better treatments for himself and others thanks to a grant from the Moffitt Cancer Center Annual Integrated Mathematical Oncology workshop.
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Congrats to lab member @AlexandriaJ_ for winning best oral presentation in computational and systems biology for her work on "Utilizing Mathematical Modeling to Investigate Health Disparities in Metastatic Prostate Cancer" at @ABRCMS 2023! Great job, Alex!
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RT @ara_anderson: A massive @mathonco congratulations to #TeamPurple for winning the 11th @imoworkshop on Steering Cancer Evolution #Moffit….
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Check out our latest publication in @CCR_AACR! We show how simple changes in insomnia can predict tumor progression with 77% accuracy, on average 45 days before the next scan in NSCLC. Led by former SPARK intern Ambika Bhatt, Matthew Schabath, Aasha Hoogland, and Heather Jim.
"Patient-Reported Outcomes as Interradiographic Predictors of Response in Non–Small Cell Lung Cancer" by @ReneeBradyPhD et al. #mathonco
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RT @Nature_NPJ: Exciting new collection at #npjSBA on mathematical oncology with a focus on cancer dynamics, guest edited by @ReneeBradyPhD….
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RT @USCSysBio_Lab: @SMB_MathBiology is celebrating diversity all summer long! . just 6 days until the first Diversity in Math Bio seminar!….
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RT @USCSysBio_Lab: join @SMB_MathBiology and the DEI Committee as we celebrate diversity in mathematical biology this summer!. learn about….
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July 2017 - PhD defense! Began postdoc @MoffittResearch with @EnderlingLab. August 2022 - Joining IMO (@mathonco) faculty in tenure-track assistant member position! . 5 years really flew by and some amazing changes are on the way. I can’t wait to see what else is in store!.
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Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data. This work was supported by @theNCI, @JKTGFoundation, and the @RoyalDames. Check out the paper and let us know your thoughts!. (10/10).
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Our analysis identifies several models with critical abilities to disentangle patient response types, together with parameter intervals where the critical points' basin of attraction might be exploited for clinical purposes. (9/10).
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Here, we compare the log-evidence of all of the models in their ability to interpret the information encrypted in an individual patient's data. The model by Elishmereni et al. (E16, panel d) performs the best for this particular patient. (8/10)
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The gorgeous tube plot shown in panel b describes the independent and dependent population trends, where the cross sections are the respective quota levels (shown individually in panel a). Model eigenvalue analyses are shown in panels c and d. (7/10)
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Here, we show how the model by Eikenberry et al. is able to beautifully disentangle patient response types. (6/10)
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Our previously published stem cell model was among one of the 13 models analyzed and was shown to describe the data well. (5/10).
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The models were calibrated to PSA data from the Canadian Prospective Phase II Trial of intermittent hormone therapy for locally advanced prostate cancer by #Bruchovsky et al. Bayesian inference was used to parse out each model's strengths and weaknesses. (4/10).
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In this study, we perform a Bayesian model comparison of 13 models of intermittent hormone therapy. The goal of the study was to determine which model could best disentangle responding and relapsing patients, while describing PSA dynamics. (3/10).
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