CRISPR-Cas is a revolutionary genome editing tool that has profoundly transformed the
field of molecular biology by affording a heightened precision in targeting a wide array
of distinct genomic sequences. One of the most prominent limitations of CRISPR-Cas
technology are unwanted off-target effects, especially off-target mutations.
Deoxyribonucleic acid mutations generated by cellular repair of a double helix break by
CRISPR-Cas action determine its phenotypic effect. It is known that mutational
outcomes are not random but depend on the deoxyribonucleic acid sequence at the target
site. The latter fact and the rapid growth of the field of artificial intelligence, the ability
of machines to perform tasks that would normally require human intelligence, have
enabled researchers to use artificial data mining, machine learning approaches and other
ways of using artificial intelligence to evaluate potential on-target and off-target effects,
creation of guide ribonucleic acid (gRNA) and analysis of edited genomes. All of this
progress is crucial for a wider use of CRISPR-Cas technology in various scientific fields.
The purpose of this diploma thesis is to examine in detail the CRISPR-Cas technology
and artificial intelligence, as well as the possibilities of integrating the latter areas to
mitigate undesired effects or eliminate the shortcomings of the CRISPR-Cas toolkit. The
aspiration is to engender a more dependable and expansive application of one of the
most transformative discoveries in the realm of biotechnology witnessed in recent
decades.
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