Final answer:
In order to find patterns of gene expression in a microarray or RNA-seq experiment, one can perform unsupervised learning using clustering techniques such as k-means clustering and hierarchical clustering. Clustering helps identify co-regulated genes or genes with similar expression patterns across different samples.
Step-by-step explanation:
In order to find patterns of gene expression in a microarray or RNA-seq experiment, one can perform unsupervised learning using clustering techniques. Clustering is a type of unsupervised learning where data points are grouped together based on their similarities or patterns. In the context of gene expression analysis, clustering can help identify genes that are co-regulated or have similar expression patterns across different samples.
One commonly used clustering algorithm for gene expression analysis is k-means clustering. This algorithm groups genes into clusters based on their expression levels across different samples. Another popular method is hierarchical clustering, which creates a dendrogram to visualize the relationships between genes and samples.
By performing unsupervised learning with clustering techniques, researchers can uncover hidden patterns and relationships in gene expression data, leading to a better understanding of biological processes and diseases.