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.


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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 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.

REFERENCE
Zanoli R et al. An annotated dataset for extracting gene-melanoma relations from scientific literature. J Biomed Semantics. 2022;13(2).
Doi: 10.1186/s13326-021-00251-3