🧠In the realm of Bioinformatics data comes in myriad forms. Clustering algorithms sift through mountains of data points, grouping them into meaningful categories based on similarities, ultimately shedding light on biological relationships, structures, and functions. Here are some clustering algorithms you should know about (and use cases too! 😎): 1️⃣ CD-HIT (Cluster Database at High Identity with Tolerance): 📚 How it works: CD-HIT clusters similar biological sequences based on sequence identity, with an adjustable threshold. 💡 Use Case: Clustering protein or nucleotide sequences to reduce redundancy and accelerate sequence searches in databases like UniProt or GenBank. 2️⃣ K-Means Clustering: 📚 K-Means partitions data into 'k' clusters by iteratively assigning each data point to the nearest cluster centroid and updating centroids based on the mean of data points in each cluster. 💡 Use Case: Segmenting gene expression data to identify distinct groups of genes with
let's Delve into Genome 🧬