brca

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

RNA-seq analysis workflow using DESeq2

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.

Relevance of genes on survival in Breast Cancer Patients

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

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.

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

A hybrid quantum machine learning platform for early disease detection

Analyzing TCGA cohorts and multiomic data in R