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

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

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

Relevance of genes on survival in Breast Cancer Patients

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

RNA-seq analysis workflow using DESeq2

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

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.

Survival Analysis Project on Breast Cancer Patients

Large-scale TCGA breast cancer gene expression analysis with PCA, immune signature scoring, and tumour expression interpretation.

Analyzing TCGA cohorts and multiomic data in R