PhysiCell: Scientist end users should use latest release! Developers please fork the development branch and submit PRs to the dev branch. Thanks!
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Updated
Jul 18, 2024 - C++
PhysiCell: Scientist end users should use latest release! Developers please fork the development branch and submit PRs to the dev branch. Thanks!
A deep learning approach to predicting breast tumor proliferation scores for the TUPAC16 challenge
This repository contains morden baysian statistics and deep learning based research articles , software for survival analysis
Chaste - Cancer Heart And Soft Tissue Environment - main public repository.
AI-based pathology predicts origins for cancers of unknown primary - Nature
Tool to visualize gigantic pathology images and use AI to segment cancer cells and present as an overlay
An R package for studying mutational signatures and structural variant signatures along clonal evolution in cancer.
🌲 An easy-to-use and scalable toolkit for genomic alteration signature (a.k.a. mutational signature) analysis and visualization in R https://shixiangwang.github.io/sigminer/reference/index.html
Cancer Predisposition Sequencing Reporter (CPSR)
This pipeline provides a way to perform pharmaceutical compounds virtual screening using similarity-based analysis, ligand-based and structure-based techniques. The pipeline contains a collections of modules to perform a variety of analysis.
Exploration of the risks of cervical cancer dataset, using supervised & unsupervised machine learning techniques to predict cervical cancer cases resulting in biopsies.
Common biochemical and topological attributes of metabolic genes recurrently dysregulated in tumors.
A unified downloader+preprocessor for cancer genomics datasets
Classifying Breast Cancer Molecular Subtypes
Image classification on lung and colon cancer histopathological images through Capsule Networks or CapsNets.
CanDI - A global cancer data integrator
📎 About MIMBCD-UI Project
Bioconductor R-package: Curated Prostate Cancer Data
This repository contains all machine learning and statistical models used to analyze the landscape of colorectal cancer.
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