[From symptom to syndrome using modern software support].

Area of Science:

• Medical Informatics
• Genomics
• Rare Diseases

Background:

• Diagnosing rare diseases presents significant clinical challenges.
• Analyzing genomic variations in rare disease patients is complex and often hinders molecular diagnostics.

Purpose of the Study:

• To provide an overview of novel, automated approaches for phenotype-driven differential diagnoses of rare diseases.
• To highlight methods for collating clinical phenotypic data and algorithms for assessing phenotypic profile similarity.
• To demonstrate the utility of combined phenotypic and genomic analysis for rare disease diagnostics.

Main Methods:

• Utilizing the Human Phenotype Ontology (HPO) for precise and comprehensive collation of phenotypic data.
• Employing algorithms, such as Phenomizer, for calculating similarity between patient phenotypes and disease descriptions.
• Integrating HPO-coded phenotypic features with genomic variation data for analysis.

Main Results:

• Ontologies enable robust automated analyses of phenotypic data.
• Digital tools facilitate differential diagnostic applications by providing similarity assessments.
• Combined analysis of phenotypic and genomic data aids in prioritizing disease-relevant genomic variations.

Conclusions:

• Automated phenotype-driven analyses using HPO are crucial for rare disease diagnosis.
• The integration of HPO and genomic data represents a significant advancement in molecular diagnostics for rare diseases.
• These approaches support phenotype-driven translational research and improve diagnostic yield.