Computacional Models practices. University of Cádiz.
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Updated
Apr 1, 2018 - Java
Computacional Models practices. University of Cádiz.
This project focuses on the segmentation of brain tumors using the Brain Tumor Segmentation (BRATs) dataset. The primary goal was to develop a deep learning model capable of accurately identifying and segmenting tumor regions in MRI scans.
This plot is useful for the comparison of mutational load across the cancer types, with input data in 2 coulmns i.e cancer types and mutatonal load for each samples in specificed cancer type.
A deep learning application that achieves brains with cancer, tumors, and aneurysms.
Classification model that can predict whether a tumor is Benign or Malignant.
Statistical analysis on Mutation Annotation Format files from Whole-Exome Sequencing of Tumor samples and Controls.
Description of the bioinformatic workflow applied in Passaro et al 2019
This project aims to model the disruptive effects of cytotoxic chemotherapeutic drugs on the immunoediting process.
Dynamics of the Tumor-Host Interactions from a Force-Field Potential
Use of classification supervised learning algorithms as a tool to perform predictive analysis over whether a mammogram mass is benign or malignant
Analysis of drug treatment on tumor volume, metastasis and survival.
Used machine learning in R to assess individual variables involved in the intra-operative pathology consultation process.
Implementation of CHOWDER method for automatic detection of ROI on gigapixel tumor images
This repository uses python to run analysis on drug regimens being tested on mice to reduce tumor sizes.
Detecting brain tumor with artificial intelligence
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