Editing Computational biology (section)

===Unsupervised Learning===
[[Unsupervised learning]] is a type of algorithm that finds patterns in unlabeled data. One example is [[k-means clustering]], which aims to partition ''n'' data points into ''k'' clusters, in which each data point belongs to the cluster with the nearest mean. Another version is the [[k-medoids]] algorithm, which, when selecting a cluster center or cluster centroid, will pick one of its data points in the set, and not just an average of the cluster.
[[File:Jmatrix.png|thumb|A heat-map of the Jaccard distances of nuclear profiles]]
The algorithm follows these steps:
# Randomly select ''k'' distinct data points. These are the initial clusters.
# Measure the distance between each point and each of the 'k' clusters. (This is the distance of the points from each point ''k'').
# Assign each point to the nearest cluster.
# Find the center of each cluster (medoid).
# Repeat until the clusters no longer change.
# Assess the quality of the clustering by adding up the variation within each cluster.
# Repeat the processes with different values of k.
# Pick the best value for 'k' by finding the "elbow" in the plot of which k value has the lowest variance.

One example of this in biology is used in the 3D mapping of a genome. Information of a mouse's HIST1 region of chromosome 13 is gathered from [[Gene Expression Omnibus]].<ref>{{cite web | url=https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE64881 | title=GEO Accession viewer }}</ref> This information contains data on which nuclear profiles show up in certain genomic regions. With this information, the [[Jaccard distance]] can be used to find a normalized distance between all the loci.