Prof. Ulf Leser (Humboldt-Universität zu Berlin)

Translational Text Mining

Text Mining has become an important tool for many areas of biomedical research. Nevertheless, its impact actually is still surprisingly small, given the enormous body of knowledge published every day and the difficulties of biomedical researchers to keep an overview of relevant developments even only in their very specific fields of research. Why has the usage of some form of semantic search engine or large-scale information extraction pipeline not yet become the standard procedure for being up-to-date wrt related work?
In this talk, I highlight some of the key issues text mining faces when it tries to become "translational", i.e., invade daily biomedical research. Examples are (a) the misleading focus on "correct extraction" where it should be "correct biological fact", (b) the wide-spread negligence of full texts and patents, and (c) the inaccessibility of typical machine learning models for end users. Notwithstanding some technical barriers, I argue that the community must invest more efforts to move closer to its users to achieve proper recognition in the field.

Bio: Ulf Leser studied computer science at the Technische Universität München and did his PhD at the Technische Universität Berlin. After positions in research institutes and in the private sector, be became a professor for Knowledge Management in Bioinformatics at Humboldt-Universität zu Berlin. His research focuses on scientific data management, statistical Bioinformatics, biomedical text mining and infrastructures for large-scale Bioinformatics analysis, topics he typically approaches in interdisciplinary projects with biologists and medical doctors. He is speaker of the graduate school SOAMED (Service-oriented architectures for medical applications) and a board member of the Berlin School for Integrative Oncology (BSIO).

Dr. Laura Furlong (Hospital del Mar Medical Research Institute, Barcelona)

Structuring knowledge on human diseases: challenges and opportunities

Recent technological breakthroughs are producing an unprecedented volume of data on the genetic determinants of human diseases. In order to better understand disease mechanisms to identify biomarkers and to support drug discovery projects, it is necessary to place these data in the context of the current biomedical knowledge. In this talk I will present our work in this area exemplified by the DisGeNET (http://www.disgenet.org/) and PsyGeNET (http://www.psygenet.org/) knowledge platforms. I will discuss issues of data silos, standardization and the importance of building an ecosystem of linked data in biomedicine to support translational research. Approaches for data curation such as community-based and crowdsourcing will be presented. Finally, I will show applications to translational bioinformatics projects, including drug target identification and unraveling of molecular basis of disease comorbidities.

Bio: Laura I. Furlong is head of the Integrative Biomedical Informatics Group, which belongs to the Research Programme on Biomedical Informatics (IMIM-UPF) and Associate Professor at the University Pompeu Fabra at Barcelona (Spain). She has a PhD in Biology from the University of Buenos Aires, Argentina and a Msc in Bioinformatics by University Pompeu Fabra. She has a broad expertise covering molecular biology, computational systems biology and text mining. Her current research lines include: a) development of new strategies and tools for knowledge extraction from biomedical literature; b) network biology for the study of human diseases and drug toxicity; c) bionformatic approaches for the reuse of clinical data for research. Her group also maintains knowledge resources to support translational research, such as the databases DisGeNET and PsyGeNET. She has published over 40 peer-reviewed articles, and act as reviewer for the journals Bioinformatics, BMC Bioinformatics, BMC Systems Biology, Database and PLOS journals. She has participated in several FP7 projects (@neurist, EU-ADR) and is currently involved in the IMI (Innovative Medicines Initiative) projects eTOX, OpenPHACTS, EMIF and IPiE and the H2020 project MedBioinformatics.