brca

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

Relevance of genes on survival in Breast Cancer Patients

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

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

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

RNA-seq analysis workflow using DESeq2

Survival Analysis Project on Breast Cancer Patients

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.

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

Analyzing TCGA cohorts and multiomic data in R