Integration of Clinical and Genomic Data for Decision Support in Cancer

2008 ◽  
pp. 768-776
Author(s):  
Yorgos Goletsis ◽  
Themis P. Exarchos ◽  
Nikolaos Giannakeas ◽  
Markos G. Tsipouras ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Decision Support ◽  
Molecular Biology ◽  
Early Diagnosis ◽  
Clinical Data ◽  
Support System ◽  
Genomic Data ◽  
Biological Information ◽  
Tumour Suppressor Genes ◽  
Suppressor Genes ◽  
Cancer Diseases

In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

Integration of Clinical and Genomic Data for Decision Support in Cancer

2011 ◽  
pp. 412-421 ◽  
Author(s):  
Yorgos Goletsis ◽  
Themis P. Exarchos ◽  
Nikolaos Giannakeas ◽  
Markos G. Tsipouras ◽  
Dimitrios I. Fotiadis
Keyword(s):  
Decision Support ◽  
Molecular Biology ◽  
Early Diagnosis ◽  
Clinical Data ◽  
Support System ◽  
Genomic Data ◽  
Biological Information ◽  
Tumour Suppressor Genes ◽  
Suppressor Genes ◽  
Cancer Diseases

In this article, we address decision support for cancer by exploiting clinical data and identifying mutations on tumour suppressor genes. The goal is to perform data integration between medicine and molecular biology by developing a framework where clinical and genomic features are appropriately combined in order to handle cancer diseases. The constitution of such a decision support system is based on (a) cancer clinical data and (b) biological information that is derived from genomic sources. Through this integration, real time conclusions can be drawn for early diagnosis, staging and more effective cancer treatment.

An Ontology-based Mediator of Clinical Information for Decision Support Systems

2008 ◽  
Vol 47 (06) ◽  
pp. 549-559 ◽  
Author(s):  
K. Ohe ◽  
Y. Kawazoe
Keyword(s):  
Decision Support ◽  
Decision Support System ◽  
Clinical Data ◽  
Support System ◽  
Adverse Drug Events ◽  
Clinical Information ◽  
Temporal Information ◽  
Temporal Abstraction ◽  
Alert System ◽  
Data Repositories

Summary Objective: We have been developing a decision support system that uses electronic clinical data and provides alerts to clinicians. However, the inference rules for such a system are difficult to write in terms of representing domain concepts and temporal reasoning. To address this problem, we have developed an ontologybased mediator of clinical information for the decision support system. Methods: Our approach consists of three steps: 1) development of an ontology-based mediator that represents domain concepts and temporal information; 2) mapping of clinical data to corresponding concepts in the mediator; 3) temporal abstraction that creates high-level, interval-based concepts from time-stamped clinical data. As a result, we can write a concept-based rule expression that is available for use in domain concepts and interval-based temporal information. The proposed approach was applied to a prototype of clinical alert system, and the rules for adverse drug events were executed on data gathered over a 3-month period. Results: The system generated 615 alerts. 346 cases (56%) were considered appropriate and 269 cases (44%) were inappropriate. Of the false alerts, 192 cases were due to data inaccuracy and 77 cases were due to insufficiency of the temporal abstraction. Conclusion: Our approach enabled to represent a concept-based rule expression that was available for the prototype of a clinical alert system. We believe our approach will contribute to narrow the gaps of information model between domain concepts and clinical data repositories.

Pathogenesis of thyroid cancer

2011 ◽  
pp. 600-609
Author(s):  
Dan Mihailescu ◽  
Arthur B Schneider ◽  
Leon Fogelfeld
Keyword(s):  
Thyroid Cancer ◽  
Molecular Biology ◽  
Chernobyl Accident ◽  
Epidemiological Studies ◽  
Thyroid Cell ◽  
Tumour Suppressor Genes ◽  
Thyroid Cells ◽  
Suppressor Genes ◽  
The Third ◽  
Biological Studies

Both epidemiological and molecular biological studies have been used to understand the origins of thyroid cancer. Epidemiological studies have been used to identify factors that predispose to thyroid cancer. That is principally how we know that exposure to radiation leads to thyroid cancer (see Chapter 3.2.5). In fact, radiation is the only environmental factor for which the proof is incontrovertible. Molecular biological studies, reviewed in the second part of this chapter, have been used to investigate the events within thyroid cells that are initiated by predisposing factors, e.g. radiation, and lead, by one or multiple steps, to transformation and cancer. These studies have focused on cancer-related genes, particularly proto-oncogenes and tumour suppressor genes, and have led to the identification of potential therapeutic agents. They have also focused on the cellular pathways and processes, including epigenetic changes and microRNA expression, which accompany transformation of the thyroid cell. Epidemiology and molecular biology have interacted productively in the studies that have followed the Chernobyl accident. This interaction is described in the third part of this chapter in which the mutations found in radiation-related thyroid cancers are reviewed.

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