How is Functional Annotation Clustering Performed?
The process of functional annotation clustering typically involves several steps: 1. Data Collection: Gathering high-throughput data such as gene expression profiles, proteomics data, or genomic sequences from cancer samples. 2. Data Preprocessing: Normalizing and filtering the data to remove noise and irrelevant information. 3. Annotation: Assigning biological functions, pathways, and interactions to the genes or proteins using databases such as Gene Ontology (GO), KEGG, and Reactome. 4. Clustering: Grouping genes or proteins based on their functional annotations using clustering algorithms like hierarchical clustering, k-means, or network-based approaches. 5. Analysis: Interpreting the clusters to identify significant biological themes and processes involved in cancer.
