Phenotypic similarity for rare disease: ciliopathy diagnoses and subtyping

Publication date: Available online 14 October 2019Source: Journal of Biomedical InformaticsAuthor(s): Xiaoyi Chen, Nicolas Garcelon, Antoine Neuraz, Katy Billot, Marc Lelarge, Thomas Bonald, Hugo Garcia, Yoann Martin, Vincent Benoit, Marc Vincent, Hassan Faour, Maxime Douillet, Stanislas Lyonnet, Sophie Saunier, Anita BurgunAbstractRare diseases are often hard and long to be diagnosed precisely, and most of them lack approved treatment. For some complex rare diseases, precision medicine approach is further required to stratify patients into homogeneous subgroups based on the clinical, biological or molecular features. In such situation, deep phenotyping of these patients and comparing their profiles based on subjacent similarities are thus essential to help fast and precise diagnoses and better understanding of pathophysiological processes in order to develop therapeutic solutions. In this article, we developed a new pipeline of using deep phenotyping to define patient similarity and applied it to ciliopathies, a group of rare and severe diseases caused by ciliary dysfunction. As a French national reference center for rare and undiagnosed diseases, the Necker-Enfants Malades Hospital (Necker Children's Hospital) hosts the Imagine Institute, a research institute focusing on genetic diseases. The clinical data warehouse contains on one hand EHR data, and on the other hand, clinical research data. The similarity metrics were computed on both data sources, and were evaluated with...
Source: Journal of Biomedical Informatics - Category: Information Technology Source Type: research