Creating a Random Forest model to predict the progression of bladder cancer

R code used for the master thesis entitled "Germline variants associated with prognosis of patients with non muscle invasive bladder cancer".

Using biological constraints to improve the performance of transcriptomic gene signatures

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"

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.

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 (unpublished).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.

This is a group project

Performing Automatic Identification and Staging of Urothelial Carcinoma in Bladder Cancer Patients Using a Hybrid Deep-Machine Learning Approach, Sarkar et al, MDPI Cancers, 2023.

Using Keras to build a deep neural network for bladder cancer progression