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BEGIN:VEVENT
DTSTAMP:20210916T132453Z
LOCATION:Henry Dunant
DTSTART;TZID=Europe/Stockholm:20210708T173000
DTEND;TZID=Europe/Stockholm:20210708T180000
UID:submissions.pasc-conference.org_PASC21_sess127_msa244@linklings.com
SUMMARY:Distilling Structured Knowledge from Cancer Biomarker Literature w
 ith INODE
DESCRIPTION:Minisymposium\n\nDistilling Structured Knowledge from Cancer B
 iomarker Literature with INODE\n\nPapadopoulos\n\nBiology is emerging as a
  data-driven science, characterized by an exponential increase in the numb
 er of published articles and other relevant resources. This abundance of u
 nstructured biomedical data is rendering the task of keeping up with the r
 apidly growing literature virtually impossible for most scientists. While 
 most biomedical articles are being processed using manual curation techniq
 ues, these methods lack the scalability to deal with ever-increasing amoun
 ts of data. Automated text mining techniques have come to the rescue in ha
 rnessing the maximum benefit from this information explosion, but they can
  be error-prone due to the complexity of natural language. The INODE EU-fu
 nded project addresses these problems in one of its use cases, focusing on
  the variability and heterogeneity of cancer genomics studies. We accelera
 te information extraction from biomarker-related articles, by leveraging a
  series of NLP methods to distill structured information in the form of&nb
 sp; Open Information Extraction (OIE) triples, represent their relationshi
 ps in a structured way, and link them with concepts from existing ontologi
 es (e.g., OncoMX). Our goal is not limited in improving cancer detection b
 y enabling the discovery of new biomarkers but it also expands to facilita
 ting efficient data exploration by domain experts lacking the necessary te
 chnical skills.\n\nDomain: CS and Math, Chemistry and Materials, Life Scie
 nces
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