ClioMD: An artificial intelligence model for ciliopathies

Abstract Cilia are highly specialized cellular organelles that serve multiple functions in human development and health. Their central importance in the body is demonstrated by the emergence of various developmental disorders resulting from defects in cilia structure and function caused by different inherited mutations in more than 150 different genes. Genomic analysis has rapidly improved our understanding of ciliopathies’ intracellular molecular biological basis over the past two decades, and new technological advances have accelerated this progress. However, most of the time, in correlation of phenotypic results with genetic variation and environmental factors, patient phenotypes do not match with the thought disease despite being a basic search in genomic medicine, candidate variants are in genes not characterized by disease, and model organisms are insufficient to explain the disease, many obstacles continue to hinder rapid and accurate diagnosis. Using advanced computing tools, artificial intelligence models can phenotypically identify overlapping disease models, such as ciliopathies, in research and diagnostic contexts. Large-scale integration of model organisms and clinical trial data can provide a wealth of knowledge unavailable in individual sources and contextualize data back to these sources. In this context, with the machine learning platform we designed, ClioMD, a program that is compatible with the HPO guideline, OMIM, GeneCards, and ClinVar databases, provides treatment and genetic counseling recommendations online in English, enabling individuals affected by ciliopathies such as Joubert syndrome, Cornelia de Lange, Bardetp Biedl syndrome, etc. to get a fast and accurate diagnosis. In conclusion, the ClioMD platform enables you to explore the relationship between phenotype and genotype for disease and as a tool to help you make an accurate diagnosis.