RDAD

Rare Disease Auxiliary Diagnosis System

Rare disease is a disease with a precious low incidence and most of them are chronic. The rare disease associated phenotypes are usually described by a set of clinical medical terms. Rare diseases always have a wide range of complex and diverse phenotypes, however, clinicians always lack of either awareness of rare diseases or clinical experiences, which makes patients with rare diseases often not be accurately diagnosed and treated in time.With the advance of medical research and the development of socio-economic, rare diseases have aroused widespread concern of researchers and clinicians. To facilitate the effective diagnosis of rare diseases, we constructed an auxiliary diagnosis system for rare diseases RDAD (phenotype-based Rare Disease Auxiliary Diagnosis system).
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To facilitate the rare disease diagnosis, we calculated the phenotypic TF-IDF-Hierarchy information content based on the phenotype semantic hierarchy of Human Phenotype Ontology (HPO), we then built the phenotypic TF-IDF-Hierarchy information content based rare disease similarity model (PICS), the phenotype-gene association based rare disease similarity model (PGAS) and the curated feature phenotype spatial vector based rare disease machine learning prediction model (CPML), as well as the curated and text mined feature phenotype spatial vector based rare disease machine learning prediction model (APML), the CPML model is the diagnostic model that we recommend.
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We have implemented our approach to build a web server that named as the RDAD. Generally speaking, the RDAD web server mainly provides two functions: rare disease information card and rare disease prediction. A rare disease information card contains three parts of the rare disease annotation, general information, phenotypic information and associated medical records. Rare disease prediction module provides the PICS model, the PGAS model, the CPML model and the APML model.
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