brca

Multi-omics machine learning project for breast cancer clinical subtype classification using TCGA-BRCA data.

We use gene expression data to perform gene set enrichments and random forest predictions to distinguish between four tumor types.

RNA-seq analysis workflow using DESeq2

Relevance of genes on survival in Breast Cancer Patients

Find tumor gene mutation rates (TP53 and STAT5A) using cancer datasets from The Cancer Genome Atlas.

RNA-seq bioinformatics pipeline identifying resistance-associated biomarkers and prognostic signatures in TCGA breast cancer transcriptomic data.

Hybrid Classical-Quantum ML system for BRCA1/BRCA2 variant classification using ClinVar data.

A hybrid quantum machine learning platform for early disease detection

Survival Analysis Project on Breast Cancer Patients

Analyzing TCGA cohorts and multiomic data in R

Interaktiv graf över ärftliga cancergenmutationer och organ/tumörrisk, baserad på RCC:s nationella vårdprogram och EAU:s prostatacancer-riktlinjer. Kod: MIT · Data: CC BY 4.0.