One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side-effects. A way to optimize treatments is to predict the recurrence probability by analysing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging and clinical evidences. This heterogeneous set of data needs a proper representation in order to be stored, computed and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted, models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot which data will populate the ontology and will be made public for further research.
Merging Person-Specific Bio-Markers for Predicting Oral Cancer recurrence through an Ontology / Dario, Salvi; Picone, Marco; Maria Teresa, Arredondo; Maria Fernanda Cabrera, Umpièrrez; Angel, Esteban; Sebastian, Steger; Poli, Tito. - In: IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING. - ISSN 0018-9294. - 60:1(2013), pp. 216-220. [10.1109/TBME.2012.2216879]
Merging Person-Specific Bio-Markers for Predicting Oral Cancer recurrence through an Ontology
PICONE, Marco;
2013
Abstract
One of the major problems related to cancer treatment is its recurrence. Without knowing in advance how likely the cancer will relapse, clinical practice usually recommends adjuvant treatments that have strong side-effects. A way to optimize treatments is to predict the recurrence probability by analysing a set of bio-markers. The NeoMark European project has identified a set of preliminary bio-markers for the case of oral cancer by collecting a large series of data from genomic, imaging and clinical evidences. This heterogeneous set of data needs a proper representation in order to be stored, computed and communicated efficiently. Ontologies are often considered the proper mean to integrate biomedical data, for their high level of formality and for the need of interoperable, universally accepted, models. This paper presents the NeoMark system and how an ontology has been designed to integrate all its heterogeneous data. The system has been validated in a pilot which data will populate the ontology and will be made public for further research.
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