Finding cold mice in scientific literature - a machine learning story

9 Novembro 2018, 14:30 • Catia Luisa Santana Calisto Pesquita

Title: Finding cold mice in scientific literature - a machine learning story

Abstract: All new drugs must be first tested with animals – typically mice – before being included in clinical studies. But the stress levels of the lab mice affect how their tumors respond to the chemotherapy drugs tested on them. In fact, independent studies in 2013 and 2015 showed that mice housed in 22°C (72°F) bioteriums became resistant to cancer drugs while mice kept at 30°C (86°F) did not. So have scientists changed the housing temperature for mice following that discovery? I will talk about the NLP strategy we used to perform a semi-automated meta-analysis aimed at investigating whether scientific behavior changes after major discoveries.



Helena Deus received her PhD in Bioinformatics from Universidade Nova de Lisboa where she focused on Linked Data and Semantic Web applications for Health Care and Life Sciences, with an emphasis on Cancer Research. Helena specializes in data integration and data wrangling techniques including sparse data management, query parallelization, data reuse and data mining for the facilitation of medical knowledge insights. Helena is passionate about breaking the cancer research silos to allow researchers to derive more value from their data and publications. Prior to joining Elsevier, Helena's roles included directing a knowledge engineering and data science team at Foundation Medicine and leading projects and strategy for Health Care and Life Sciences at the Digital Enterprise Research Institute, National University of Ireland at Galway (DERI/NUIG). Helena has published over 30 peer reviewed papers and was one of the winners of the Big Data Track in the 2013 Semantic Web Challenge and of the Linked Data Cup with her work on linking data from The Cancer Genome Atlas.