Artificial Intelligence in Cancer Research: Knowledge Representation and Data Perspectives

Dear Colleagues,

Cancer research relies on a large number of diverse datasets generated by different omics technologies. Electronic health record data, which are gathered at the point of care, are increasingly utilized in pre-clinical and clinical research as well as health management and analyzed in unison with genomic data.

The curation, management, and analysis of these data present unique challenges arising from data heterogeneity, complexity, and size. Artificial intelligence techniques are being increasingly adopted to address these issues, both at the level of knowledge representation, with ontologies and knowledge graphs playing a central role, and at the data analysis level, with sophisticated machine learning approaches that tackle data complexity challenges. Moreover, the integration of knowledge representation with data analytics and machine learning is becoming an increasingly hot topic due to its potential to support explainability and promote multidisciplinary cancer research efforts.

This Special Issue will focus on the challenges afforded by the size, complexity, and heterogeneity of data in cancer research and care, with a focus on artificial intelligence both from the knowledge representation and data perspectives. This includes, but is not limited to, ontology development and evolution, genomic data analysis, ontology-based machine learning and artificial intelligence, semantic data integration using knowledge graphs and other methods, machine learning for network data, machine learning for complex data, use of artificial intelligence in translational medicine, and clinical decision support.

Dr. Catia Pesquita
Dr. Andreas Schlicker
Guest Editors

Manuscript Submission Information

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