KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps

Abstract

Cytogenetics is a field of genetics investigating the relationships between the hereditary characteristics, structure and behavior of human chromosomes, as well as the medical and evolutionary repercussions of chromosomal abnormalities. Detecting the human karyotype and chromosomal anomalies could offer relevant information about human genetics and possible genetic disorders. This paper investigates an automatic solution for chromosomes classification and introduces an unsupervised learning approach KarySOM based on self-organizing maps for the problem of automatically human karyotyping, with the more general goal of uncovering chromosomal anomalies. The experimental evaluation of the proposed approach highlights its effectiveness for unsupervised classification of human chromosomes.