• Artificial Intelligence in Medicine

Invited SpeakersProfile Details

Senay Kafkas
Senay Kafkas Senay Kafkas is a Research Scientist at CBRC, KAUST.

Biography

 Senay Kafkas holds a PhD in computer engineering and is specialized on biomedical text mining. She works as a research scientist in Computational Bioscience Research Center at King Abdullah University of Science and Technology in Thuwal, Saudi Arabia. Her research focuses on development of Artificial Intelligence (AI) based methods for mining health relevant information and supporting disease diagnostic. Before joining KAUST, Senay worked as a text mining specialist at the European Bioinformatics Institute, near Cambridge in the United Kingdom. Her role involved managing R&D projects linked to the Europe PMC literature database and the industry. Senay has over 40 publications in the area of biomedical data mining.


All sessions by Senay Kafkas

  • Day 3Wednesday, February 20th
Session 5 : AI and Knowledge Mining from Text, Clinical Data, and Bio-imaging (Chair - Xin Gao)
11:00 am

Use of ontologies and text mining in biomedicine: applications to infectious disease research

The volume of literature data increases tremendously which makes it inevitable to apply text mining methods to retrieve, manage and analyse the information. Ontologies represent information semantically by describing the knowledge as a set of concepts within specific domains and the relationships between these concepts. Text mining profits from biomedical ontologies in different tasks, especially in mapping ontology concepts to terms in text (e.g. named entity recognition) since they provide domain knowledge. Ontology development profits from automated text mining methods since publications contain references to existing terms and concepts.

In this talk, I will discuss different approaches in which ontologies and text-mining methods have been used in biomedicine with a special focus on infectious diseases. More specifically, I will present on the development of PathoPhenoDB - a database containing pathogen-disease phenotype relations for supporting infectious disease research; use of text mining for expanding the Disease Ontology and use of ontologies and text mined data for predicting host-pathogen interactions.

Building 9 - Lecture hall 2 11:00 - 11:35 Details