Research Overview:

Gene therapy research aims to treat genetic diseases by targeting disease-causing variants. However, many genetic variants related to neurological and sensory-related conditions have not been classified, making disease prognosis difficult for genetic counseling and as future candidates for gene therapy.

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Advancements in the field of Artificial Intelligence (AI)-based protein structure determination have provided new opportunities to tackle complex biomedical challenges. In this study, we are taking advantage of these advancements and of the available genomic and structural data to propose a novel approach to classify disease-causing variants, in genes related to hearing loss and neurological conditions.

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Our objective is to develop AI-based methodologies for variant pathogenicity prediction by describing their functional impact at the protein level. To this end, we have collected a large dataset of known genetic variants in hearing-impaired patients and have applied several

algorithmic tools to extract structural, physicochemical, and evolutionary features, in addition to protein-protein interactions data. This data was used for the development of machine learning-based methodologies, including gene-specific, protein-type-specific and general predictors for genetic variants in known deafness genes.

Our approach aims to improve the accuracy and speed of variant classification, which can ultimately aid in the development of more effective and personalized gene therapy treatments for neurological and sensory medical conditions.

Research in collaboration with: Zippora Brownstein, Juan Fernandez-Recio, Xavier de La Cruz, Fabian Glaser. Department of Human Molecular Genetics and Biochemistry, Faculty of Medical and Health Sciences and Sagol School of Neuroscience, Tel Aviv University, Tel Aviv, Israel.
Instituto de Ciencias de la Vid y del Vino (ICVV), CSIC-UR-Gobierno de La Rioja, Logroño, Spain. Research Unit in Clinical and Translational Bioinformatics, Vall d'Hebron Institute of Research (VHIR), Universitat Autònoma de Barcelona, Barcelona, Spain & Institut Catala per la Recerca i Estudis Avançats (ICREA), Barcelona, Spain. THHI, Technion Human Health Initialive, Israel Institute of Technology, Haifa, Israel