February 24, 2020
- February 26, 2020 till February 26, 2020
- IIT, Lecture Hall
- Presentation
Member of BioHIT team, Vasileios Konstantakos, Doctor of Medicine, will give a presentation on “CRISPR Guide Design: An overview of predictive tools and the role of Deep Learning”, at the Lecture Hall of the Institute of Informatics and Telecommunications, NCSR Demokritos on Wednesday 26/2, at 14:00.
Abstract
The CRISPR/Cas9 system has revolutionized the field of genome editing and promises the ability to probe genetic interactions at their origin and the opportunity to cure severe inherited diseases. The CRISPR/Cas9 system identifies a specific site by the complementarity between the guide RNA and the DNA target sequence, which is less expensive and time-consuming compared with previous gene-editing tools, as well as more precise and scalable. However, low cleavage efficiency and off-target effects hamper the development and application of CRISPR/Cas systems. To predict cleavage efficiency and specificity, numerous computational approaches have been developed for scoring guide RNAs. Nonetheless, currently available tools cannot robustly predict experimental success as prediction accuracy depends on the approximations of the underlying model and how closely the experimental setup matches the data the model was trained on. Here, we present an overview of the available computational tools, their current limitations, and future considerations, focusing on recent approaches based on Deep Learning.
Speaker Bio
Vasileios Konstantakos holds an M.D. degree from the National and Kapodistrian University of Athens. During his studies, he developed an interest in the applications of Machine Learning in Healthcare. Currently, he is working with the BioHIT team in the evaluation and improvement of Machine-Learning tools for CRISPR gRNA design. He is also working under the supervision of Georgios Tsivgoulis at ‘Attikon’ University Hospital, focusing on diagnostic and prognostic biomarkers in stroke. His research interests are focused on Bioinformatics/ Machine Learning in Medicine and Neuro-Immunology/ Vascular Neurology.
