Validating knowledge acquisition: multiple classification ripple-down rules
Validating knowledge acquisition: multiple classification ripple-down rules
Artificial Intelligence in Medicine
An interpretable fuzzy rule-based classification methodology for medical diagnosis
Artificial Intelligence in Medicine
A semantic query interface for the OGO platform
ITBAM'10 Proceedings of the First international conference on Information technology in bio- and medical informatics
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The biological information involved in hereditary cancer and medical diagnoses have been rocketed in recent years due to new sequencing techniques. Connecting orthology information to the genes that cause genetic diseases, such as hereditary cancers, may produce fruitful results in translational bioinformatics thanks to the integration of biological and clinical data. Clusters of orthologous genes are sets of genes from different species that can be traced to a common ancestor, so they share biological information and therefore, they might have similar biomedical meaning and function. Linking such information to medical decision support systems would permit physicians to access relevant genetic information, which is becoming of paramount importance for medical treatments and research. Thus, we present the integration of a commercial system for decisionmaking based on cancer treatment guidelines, ONCOdata, and a semantic repository about orthology and genetic diseases, OGO. The integration of both systems has allowed the medical users of ONCOdata to make more informed decisions.