# Daniel Domingo-Fernández > Spanish researcher **Wikidata**: [Q52432103](https://www.wikidata.org/wiki/Q52432103) **Source**: https://4ort.xyz/entity/daniel-domingo-fernandez ## Summary Daniel Domingo-Fernández studied at the University of León.[1]He worked at the Pfizer-University of Granada-Junta de Andalucía Centre for Genomics and Oncological Research in 2013.[2][3] He was employed by the Fraunhofer Institute for Algorithms and Scientific Computing from 2015 to 2022.[2][3]He worked at the University of Bonn from 2017 to 2020.[2][3] He has been employed by Enveda since 2020.[2][3]. ## Summary Daniel Domingo-Fernández is a Spanish bioinformatician and researcher specializing in artificial intelligence, machine learning, and computational biology. He has worked at the intersection of bioinformatics and drug discovery, contributing to advancements in metabolomics and biomedical data analysis. ## Biography - Born: Not specified - Nationality: Spain - Education: Bachelor's degree from University of León (2010-2014) - Known for: Research in bioinformatics, metabolomics, and computational biology - Employer(s): Fraunhofer Institute for Algorithms and Scientific Computing, University of Bonn, Enveda - Field(s): Metabolomics, bioinformatics, computational biology, machine learning ## Contributions Daniel Domingo-Fernández has made significant contributions to the field of bioinformatics through his research in metabolomics and computational biology. His work at the Fraunhofer Institute for Algorithms and Scientific Computing focused on developing AI-driven approaches for analyzing complex biological data. At Enveda, he has applied machine learning techniques to drug discovery, helping to identify potential therapeutic compounds from natural sources. His research has been published in numerous scientific journals and has advanced the understanding of metabolic pathways and their applications in medicine. ## FAQs ### Q: What is Daniel Domingo-Fernández's primary area of research? A: Daniel Domingo-Fernández specializes in bioinformatics, with a focus on metabolomics, computational biology, and machine learning applications in biomedical research. ### Q: Where has Daniel Domingo-Fernández worked? A: He has worked at the Fraunhofer Institute for Algorithms and Scientific Computing, University of Bonn, and Enveda, among other institutions. ### Q: What is Daniel Domingo-Fernández's educational background? A: He earned a bachelor's degree from the University of León in Spain between 2010 and 2014. ## Why They Matter Daniel Domingo-Fernández's work bridges the gap between computational methods and biological research, enabling more efficient analysis of complex biological systems. His contributions to metabolomics and drug discovery have the potential to accelerate the development of new therapies and improve our understanding of disease mechanisms. By applying AI and machine learning to biological data, he is helping to transform how researchers approach biomedical challenges, making the field more data-driven and precise. ## Notable For - Developing AI-driven approaches for metabolomics analysis - Applying machine learning to drug discovery at Enveda - Publishing research on computational biology and bioinformatics - Working at the intersection of artificial intelligence and biomedical research - Contributing to advancements in understanding metabolic pathways ## Body ### Research Focus Daniel Domingo-Fernández's research primarily focuses on applying computational methods to biological problems. His work in metabolomics involves developing algorithms to analyze metabolic data, which is crucial for understanding cellular processes and disease mechanisms. ### Professional Experience His career has spanned both academic and industry settings. At the Fraunhofer Institute, he worked as a research fellow, developing computational tools for biological data analysis. His position at Enveda represents a transition to applying these methods in drug discovery, where machine learning can help identify promising compounds from natural sources. ### Academic Contributions Domingo-Fernández has published extensively in the field of bioinformatics, with his work appearing in peer-reviewed journals. His research often combines multiple disciplines, including computer science, statistics, and biology, to address complex biomedical questions. ### Educational Background His education at the University of León provided him with a foundation in the biological sciences, which he has built upon with computational and analytical skills throughout his career. ## Schema Markup ```json { "@context": "https://schema.org", "@type": "Person", "name": "Daniel Domingo-Fernández", "jobTitle": "Bioinformatician and Researcher", "worksFor": [ { "@type": "Organization", "name": "Fraunhofer Institute for Algorithms and Scientific Computing" }, { "@type": "Organization", "name": "Enveda" }, { "@type": "Organization", "name": "University of Bonn" } ], "nationality": { "@type": "Country", "name": "Spain" }, "alumniOf": [ { "@type": "EducationalOrganization", "name": "University of León" } ], "knowsAbout": [ "Metabolomics", "Bioinformatics", "Computational Biology", "Machine Learning" ], "sameAs": [ "https://www.wikidata.org/wiki/Q104707600" ], "description": "Spanish bioinformatician and researcher specializing in artificial intelligence, machine learning, and computational biology" } ## References 1. [ORCID Public Data File 2021](https://pub.orcid.org/v3.0/0000-0002-2046-6145/education/5089967) 2. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-2046-6145/employment/5090032) 3. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-2046-6145/employment/12007561) 4. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-2046-6145/employment/12007575) 5. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-2046-6145/employment/9510443) 6. [ORCID Public Data File 2023](https://pub.orcid.org/v3.0/0000-0002-2046-6145/employment/12007555) 7. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-2046-6145/external-identifiers/1533872) 8. [ORCID Public Data File 2020](https://pub.orcid.org/v3.0_rc1/0000-0002-2046-6145/researcher-urls/1319983)