Gabriel Cabas is a bioinformatics engineer with a professional focus on applying deep learning and machine learning in bioinformatics. His experience spans protein engineering, genomics, epigenetics, and histological image analysis, where he leverages advanced algorithms to address complex scientific challenges and develop innovative solutions in biomedicine and biotechnology.

Throughout his career, he has worked in protein engineering, applying deep learning techniques to predict and design protein sequences. He also has hands-on experience in histological image analysis, using AI to analyze tissue samples and identify patterns that support medical diagnosis. In addition, he works with epigenetic data, integrating regulatory layers into computational analyses to better understand gene expression and disease mechanisms.

He is also increasingly focused on the application of machine learning in genomics, particularly in genome-wide association studies, polygenic risk scores, and omics data analysis. His work aims to improve the prediction of genetic variants, identify risk factors for complex diseases, and contribute to the personalization of treatments in precision medicine.

He seeks to collaborate on innovative projects that integrate engineering, computational biology, artificial intelligence, and medical imaging, with a focus on developing solutions that generate meaningful impact in science and healthcare.