MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets
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Updated
May 20, 2022 - Java
MetaOmGraph: a workbench for interactive exploratory data analysis of large expression datasets
Exercises solved for the Practical Statistics Module of Udacity's DAND: assignments and practice problems
Open-source software pipeline for cancer classification from high-throughput data using machine learning.
We used different machine learning approaches to build models for detecting and visualizing important prognostic indicators of breast cancer survival rate. This repository contains R source codes for 5 steps which are, model evaluation, Random Forest further modelling, variable importance, decision tree and survival analysis. These can be a pipe…
A Platypus-based variant calling pipeline for cancer data
Autoencoders - a deep neural network was used for feature extraction followed by clustering of the "Cancer" dataset using k-means technique
A Machine Learning Classifier for Lung & Colon Cancer Histopathological Images.
The first GANs-based omics-to-omics translation framework
Bioconductor R-package: Curated Prostate Cancer Data
Helping cancer patients find a second opinion anywhere in the US within seconds.
An example of predicting breast cancer using existing data to learn with decision trees (scikit-learn/python)
Improving Information Extraction from Pathology Reports using Named Entity Recognition
A partir da Cadeira de Introdução a Ciência de Dados (ICD), com o Professor Yuri Malheiros, na Universidade Federal da Paraíba (UFPB), nós, Adriel, Jessica e Kamily, faremos uma analise dos dados estatísticos de casos de câncer, relacionados a certas idades, a fim de responder perguntas pré-definidas.
Examples on processing and working with TCGA mutation and RNA-Seq data
A comprehensive comparison of decision tree and random forest for cancer classification.
Open-source command-line pipeline for cancer type classification of high-throughput data using machine learning.
In this project I will look at a dataset of patient data relating to breast cancer, and develop a machine learning model that will aim to predict Malignant tumors with the highest accuracy.
Predicting chemosensitivity using gene expression
A Python client for the cbioPortal Cancer API.
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