Unit:
Κατεύθυνση ΒιοπληροφορικήLibrary of the School of Science
Author:
Tatsi Konstantina
Supervisors info:
Αλέξανδρος Γεωργακίλας, Αναπληρωτής Καθηγητής, Σχολή Εφαρμοσμένων Μαθηματικών & Φυσικών Επιστημών, Εθνικό Μετσόβιο Πολυτεχνείο
Κωνσταντίνος Ε. Βοργιάς, Καθηγητής, Τμήμα Βιολογίας, Εθνικό και Καποδιστριακό Πανεπιστήμιο Αθηνών
Παντελής Μπάγκος, Αναπληρωτής Καθηγητής, Τμήμα Πληροφορικής με Εφαρμογές στη Βιοϊατρική, Πανεπιστήμιο Θεσσαλίας
Original Title:
Ανάλυση Μικροσυστοιχιών RΝΑ στην περίπτωση έκθεσης σε Σωματιδιακή Ακτινοβολία με έμφαση στην Ραδιοθεραπεία.
Translated title:
Analysis of RNA Microarrays in case of exposure to Particle Radiation with Emphasis on Radiotherapy.
Summary:
Ionizing radiation is defined as the energy that passes through matter, causes changes in the molecular bonds of atoms and leads to the formation of ions. Many studies to date describe its effect on biological systems and its multiple positive applications in diagnosing and fighting human diseases (mainly cancer). However, ionizing radiation remains an extremely harmful and often fatal agent for the matter, as it destabilises and destroys organic molecules, most notably by causing exogenous DNA damages that often lead to carcinogenesis.
The aim of this work was to investigate the existence of specific biomarkers and their correlation with specific doses of radiation. For this reason, microarray data was analyzed from experiments where human cells were exposed to different quantities and species of radiation and were harvested at different times after irradiation. These data were analyzed with the InSybio platform using statistical methods and algorithms. Networks were analyzed for each type of radiation and time, finding genes with statistical significance and correlation with diseases. Finally, a comment was made on the important genes of each class.
Main subject category:
Science
Keywords:
ionizing radiation, low doses, high doses, differential gene expression, bioinformatics, microarray analysis, databases, meta-analysis, Βιοδείκτεςs, biological networks
