Using ontology to mine and classify Li-Fraumeni Syndrom patients

The Li-Fraumeni Syndrome (LFS) is a syndrome that causes multiple primary tumors in children and young adults. The main motivation of this work is to create a single integrated system that allows doctors and researchers from the A.C. Camargo Cancer Center to relate family histories, clinical and molecular data present in di erent databases through an innovative data integration methodology in order to improve the existing LFS diagnose criteria, or even to propose a new set of clinical criteria. (Párrafo extraído del texto a modo de resumen)Sociedad Argentina de Informática e Investigación Operativa (SADIO

Research Data Curation and Management Bibliography

This e-book includes over 800 selected English-language articles and books that are useful in understanding the curation of digital research data in academic and other research institutions. It covers topics such as research data creation, acquisition, metadata, provenance, repositories, management, policies, support services, funding agency requirements, open access, peer review, publication, citation, sharing, reuse, and preservation. It has live links to included works. Abstracts are included in this bibliography if a work is under certain Creative Commons Licenses. This book is licensed under a Creative Commons Attribution 4.0 International License. Cite as: Bailey, Charles W., Jr. Research Data Curation and Management Bibliography. Houston: Digital Scholarship, 2021

Use of Ontologies for Data Integration and Curation

Data curation includes the goal of facilitating the re-use and combination of datasets, which is often impeded by incompatible data schema. Can we use ontologies to help with data integration? We suggest a semi-automatic process that involves the use of automatic text searching to help identify overlaps in metadata that accompany data schemas, plus human validation of suggested data matches. Problems include different text used to describe the same concept, different forms of data recording and different organizations of data. Ontologies can help by focussing attention on important words, providing synonyms to assist matching, and indicating in what context words are used. Beyond ontologies, data on the statistical behavior of data can be used to decide which data elements appear to be compatible with which other data elements. When curating data which may have hundreds or even thousands of data labels, semi-automatic assistance with data fusion should be of great help. 1