Welcome to SEED -- preliminary Screening-Enrichment analysis-Eliminate irrelevant genes-develop a model

SEDM is a tool that utilizes RNAseq expression matrices in combination with multiple prognostic indicators. It applies machine learning and gene function analysis to identify potential tumor gene signatures from high-dimensional RNAseq data.

This platform allows you to:

• Identify Tumor Gene Signatures: Use RNAseq expression data combined with prognostic indicators to find potential gene signatures linked to tumor prognosis.
• Analyze Datasets and Select Features: Preprocess data, perform dimensionality reduction, and select key features to discover meaningful gene expression patterns.
• Build and Validate Machine Learning Models: Develop models to predict the prognostic value of genes and validate them using single-cell sequencing and mIHC data.