Martin Hofmann-Apitius

Short biography Prof. Dr. Martin Hofmann-Apitius Martin Hofmann-Apitius holds a PhD in Molecular Biology and worked for more than 10 years in experimental molecular biology. The screening for novel genes involved in tumour metastasis lead him into the area of functional genomics and subsequently to applied bioinformatics. Martin Hofmann-Apitius has experience in both, academic (University of Heidelberg (ZMBH), Forschungszentrum Karlsruhe (ITG), German Cancer Research Center (DKFZ)) and industrial (BASF, Boehringer Ingelheim, LION bioscience AG) research. Since 2002 he is leading the Department of Bioinformatics at the Fraunhofer Institute for Algorithms and Scientific Computing (SCAI) in Sankt Augustin (Germany), a governmental non-profit research institute. In July 2006 he has been appointed as a Professor for Applied Life Science Informatics at Bonn-Aachen International Center for Information Technology (B-IT). Martin Hofmann-Apitius is (co-) author of more than 160 scientific publications. Major scientific contributions were the cloning and identification of CD44v, the first gene that mediates metastatic potential to tumour cells, the functional annotation of the mouse transriptome, and information extraction methodology used for the semi-automated generation of the first comprehensive, computable model for Alzheimer´s Disease. Martin Hofmann-Apitius was the academic initiator and co-coordinator of IMI-project AETIONOMY, a project aimed at generating a mechanism-based taxonomy of neurodegenerative diseases (see www.aetionomy.org). He is also involved in related IMI-projects, such as EPAD (www.ep-ad.org), PHAGO (www.phago.eu) and RADAR-AD (https://www.radar-ad.org) Current research activities at the Department of Bioinformatics at Fraunhofer SCAI focus on: • Automated methods for the extraction of relevant information from unstructured information sources such as journal publications, patents and web-based sources • Integrative semantics, data- and knowledge-integration in neurodegeneration research • Knowledge-based mechanistic modelling of neurodegenerative diseases • Mining in real-world data (social networks, patient forums, electronic patient records) • Longitudinal disease (risk) models and their use in Virtual Patient Cohorts