Introduction to Unsupervised Learning in Bioinformatics

Summary

Unsupervised learning algorithmic techniques are applied in grouping the data depending upon similar attributes, most similar patterns, or relationships amongst the dataset points or values. These Machine learning models are also referred to as self‐organizing models which operate on clustering technique. Distinct approaches are employed on every other algorithm in splitting up data into clusters. Unsupervised machine learning uncovers previously unknown patterns in data. Unsupervised machine learning algorithms are applied in case of data insufficiency. Few applications of unsupervised machine learning techniques include: Clustering, anomaly detection. Clustering algorithms in bioinformatics are mostly used to decrypt the salient data in gene expression which is used to acknowledge biological processes in an organism. These models aid in drug design through gene expression profiling. Self organising maps are used in data reduction which provides a better understanding of genomics. Various clustering algorithms are deployed in microarray analysis which is useful in clinical research in keeping track of gene expression data. To define in simpler terms unsupervised learning is a technique which works on the input data to produce the output which is hidden or undetermined. This chapter presents various unsupervised algorithms used for knowledge exploration in the field of bioinformatics and highlights several novel works reported in the recent literature.