Creating a Random Forest model to predict the progression of bladder cancer
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Updated
Jun 8, 2019 - R
Creating a Random Forest model to predict the progression of bladder cancer
Using Keras to build a deep neural network for bladder cancer progression
This is a group project
Image analysis pipelines for double stained urothelial carcinoma samples featuring the watershed-based algorithm and template matching techniques.
A new tool to predict early-stage bladder cancer recurrence and progression. The application uses advanced artificial intelligence to combine state-of-the-art scales, outperforms them, and is freely available online.
Analyses and figures related to Mossanen and Carvalho et al Eur Urol 2021 manuscript entitled "Genomic Features of Muscle-Invasive Bladder Cancer Arising After Prostate Radiotherapy"
Using biological constraints to improve the performance of transcriptomic gene signatures
R code used for the master thesis entitled "Germline variants associated with prognosis of patients with non muscle invasive bladder cancer".
Executable of trained model presented in Boll&Vázquez Montes de Oca et al. (unpublished).
In-house bladder cancer data analysis from a collaborative project between Dhaka University, Bangladesh and IARC, Lyon, France.
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