RNA-seq generation model | Paper | Code
Modified it to Pytorch (gene level self-attention) | Code
Attention-based architecture for diagnosis and prognosis from omics data | Code
BRCA_weighted_DEGs.R - Top 1,000 genes with high weights were selected from the miRNA-TF-mRNA gene regulatory networks.
- Reference Statistical Analysis | Paper
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Subtypes Basal-like Her2 LumA LumB Normal-like Weighted DEGs 376 157 249 206 249
- GTEx(Genotype-Tissue Expression) Dataset
- TCGA(Cancer Genome Atlas) Dataset
- L1000 landmark gene set
- RNA-seq(human transcriptomics) Dataset (9147 samples and 18154 genes)
- TCGA-BRCA (PAM50)
- torch >= 1.12.1
- umap-learn >= 0.5.3
- scikit-learn >= 1.1.1