AI-powered extraction of biomedical relationships from the literature

Melanoma is among the most serious types of skin cancers. To understand and fight the disease, it is essential to identify the genes that are involved. What if such information could be automatically retrieved and annotated from research literature? In this in silico talk, SIB Group Leader Fabio Rinaldi from the Dalle Molle Institute for Artificial Intelligence Research (IDSIA) explains how to leverage Natural Language Processing techniques to support this goal. You will learn about the methods he and his colleagues used to uncover over 2,000 new genes associated with melanoma for their latest paper, published in the Journal of Biomedical Semantics, and how to navigate the database they constructed. If you are a melanoma researcher, or a machine learning developer, this in silico talk is for you.

About the in silico talks series – The latest in bioinformatics by SIB Scientists

The in silico talks online series aims to inform bioinformaticians, life scientists and clinicians about the latest advances led by SIB Scientists on a wide range of topics in bioinformatics methods, research and resources. Stay abreast of the latest developments, get exclusive insights into recent papers, and discover how these advances might help you in your work or research, by subscribing to the in silico talks mailing list.

Speaker

Name:

Fabio Rinaldi

Institute:

Dalle Molle Institute for Artificial Intelligence Research

Group:

BioMeXT Biomedical Information Extraction

Dr. Fabio Rinaldi leads the Natural Language Processing (NLP) research group at the Dalle Molle Institute for Artificial Intelligence Research (IDSIA). Previously he was a senior researcher at the Institute of Computational Linguistics of the University of Zurich, where he initiated his biomedical text mining group. An important international collaboration in recent years was with the RegulonDB group at the National University of Mexico, where he was visiting research professor. RegulonDB is one of the major life sciences databases, funded by the US National Institute of Health.

He acquired and managed numerous projects, with funding from the European Union, the Swiss National Science Foundation, InnoSuisse, large companies (in particular the Pharmaceutical Industry), and private research foundations. He has an academic background in computer science and more than 25 years experience in NLP research, with a specific focus on applications in the biomedical domain, such as automatic analysis of the scientific literature, of clinical reports, and health-related social media discussions. He also authored more than 100 scientific papers in various areas of NLP.

In addition to his role at IDSIA, he is a group leader at the Swiss Institute of Bioinformatics (SIB), and he is affiliated with the Fondazione Bruno Kessler (Trento, Italy), and the Department of Quantitative Biomedicine (DQBM) of the University of Zurich.