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Great success of the PhD doctoral thesis at the Big Data Talent Awards 2016


Fri, 21/10/2016 - 16:22

Great success of the PhD doctoral thesis at the Big Data Talent Awards 2016

Maria Chatzou, PhD student at the Comparative Bioinformatics laboratory has been recently awarded the Big Data Talent Award 2016 in the category of best doctoral thesis.

“I feel really honored I won the prize and very glad to see that people are now realizing that Biology is not anymore just a Life Science but equally a Data Science. I believe that Big Bio Data will be fundamental in shaping the future of precision medicine and revolutionizing healthcare,” stated the awarded scientist Maria Chatzou at the end of the award ceremony.

The purpose of her thesis titled “Large-scale comparative bioinformatics analyses“ was to explore the impact of large-scale data analysis on multiple sequence alignment and phylogenetic reconstruction, two of the most popular modeling methods in biology.

Her approach will shed light to the new medial and research trends leading to personalised and precision medicine, involving large-scale analyses based in genome sequencing and biological data. All these opportunities raise up also many important issues, the most pressing ones being reliability and reproducibility.

Maria and colleagues have found instability, which may have important consequences for personalised medicine, owing to the growing importance of these methods in treatments relying on personalised genomics analyses. In her thesis Maria developed these issues and their consequences. She also introduced solutions developed by herself, in the form of a new generation of computational tools allowing efficient and reproducible computation of complex pipeline based analyses.

The Bioionformatics and Genomics programme has been really successful in the 2016 edition of the Big Data Talent Awards. Apart from Maria Chatzou, Davide Cirillo at the Gene Function and Evolution laboratory was also distingued finalist in the category of best doctoral thesis.  His thesis titled "Prediction of protein and nucleic acid interactions" was focused in developing high-performance bioinformatic methods to quantitative measure and predict the nucleic acids and protein interactions.