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    \textbf{Abstract}

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The subject of this work is the domain of machine learning applied in the medical domain. In this thesis, we provide a new approach, to the best of our knowledge, for \emph{human karyotyping} using \emph{Self-Organizing Maps}. 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 like Down syndrome.

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The thesis is structured in three chapters. The first chapter gives an introduction to artificial intelligence, starting from its beginnings all the way to Self-Organizing Maps. The second chapter introduces a new approach based on Self-Organizing Maps for human karyotyping problem. The purpose of the last chapter is to provide more details regarding the software application, \emph{KarySOM}, presented in the second chapter.

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The main contribution of the thesis consists in introducing a new approach for human karyotyping using Self-Organizing Maps, called \emph{KarySOM}. Our major goal it to provide a new method for an automated karyotyping system using an unsupervised technique, Self-Organizing Maps. In order to emphasize the effectiveness of our approach, several experiments were performed and also the obtained results were evaluated using the $PrecKar$ score described in Chapter 2. The potential of unsupervised learning models for human karyotyping was highlighted through the obtained computational results and also confirmed from a biological perspective.

The research conducted in the thesis represents a starting point for future investigations regarding the use of machine learning for detecting chromosomal abnormalities.

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The original part of this thesis is contained in Chapter 2 and was published in the research paper

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\textbf{Casian-Nicolae Marc} and Gabriela Czibula.
\newblock {\em KarySOM: An Unsupervised Learning based Approach for Human Karyotyping using Self-Organizing Maps}.
\newblock Proceedings of the 14th International conference on Intelligent Computer Communication and Processing, Cluj-Napoca, Romania,  2018, under review (\textbf{ISI Proceedings}).

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This work is the result of my own activity. I have neither given nor received unauthorized assistance on this work.

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\textbf{Cluj-Napoca, 25.06.2018}
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\textbf{Marc Casian-Nicoale} \\
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