PhenoScore: AI-based phenomics to quantify rare disease and genetic variation
Dingemans, A. J. M.; Hinne, M.; Truijen, K. M. G.; Goltstein, L.; van Reeuwijk, J.; de Leeuw, N.; Schuurs-Hoeijmakers, J.; Pfundt, R.; Diets, I. J. M.; den Hoed, J.; de Boer, E.; Coenen-van der Spek, J.; Jansen, S.; van Bon, B. W.; Jonis, N.; Ockeloen, C.; Vulto-van Silfhout, A. T.; Kleefstra, T.; Koolen, D. A.; Campeau, P. M.; Palmer, E. E.; Van Esch, H.; Lyon, G. J.; Alkuraya, F. S.; Rauch, A.; Marom, R.; Baralle, D.; van der Sluijs, P. J.; Santen, G. W. E.; Kooy, R. F.; van Gerven, M. A. J.; Vissers, L. E. L. M.; de Vries, B. B. A.
Show abstract
While both molecular and phenotypic data are essential when interpreting genetic variants, prediction scores (CADD, PolyPhen, and SIFT) have focused on molecular details to evaluate pathogenicity -- omitting phenotypic features. To unlock the full potential of phenotypic data, we developed PhenoScore: an open source, artificial intelligence-based phenomics framework. PhenoScore combines facial recognition technology with Human Phenotype Ontology (HPO) data analysis to quantify phenotypic similarity at both the level of individual patients as well as of cohorts. We prove PhenoScores ability to recognize distinct phenotypic entities by establishing recognizable phenotypes for 25 out of 26 investigated genetic syndromes against clinical features observed in individuals with other neurodevelopmental disorders. Moreover, PhenoScore was able to provide objective clinical evidence for two distinct ADNP-related phenotypes, that had already been established functionally, but not yet phenotypically. Hence, PhenoScore will not only be of use to unbiasedly quantify phenotypes to assist genomic variant interpretation at the individual level, such as for reclassifying variants of unknown clinical significance, but is also of importance for detailed genotype-phenotype studies.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Genome sequencing reveals the impact of non-canonical exon inclusions in rare genetic disease 94%
- Towards robust clinical genome interpretation: developing a consistent terminology to characterize disease-gene relationships - allelic requirement, inheritance modes and disease mechanisms 94%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 93%
Similar papers in this journal
- Polycomb-associated and Trithorax-associated developmental conditions – phenotypic convergence and heterogeneity 94%
- Structural variant calling and clinical interpretation in 6224 unsolved rare disease exomes 93%
- BCL11A intellectual developmental disorder: defining the clinical spectrum and genotype-phenotype correlations 92%
Similar papers in this journal
- Using single molecule Molecular Inversion Probes as a cost-effective, high-throughput sequencing approach to target all genes and loci associated with macular diseases 94%
- Matching whole genomes to rare genetic disorders: Identification of potential causative variants using phenotype-weighted knowledge in the CAGI SickKids5 clinical genomes challenge 93%
- REVEL is better at predicting pathogenicity of loss-of-function than gain-of-function variants 93%
Similar papers in this journal
- Advancing Genotype-Phenotype Analysis through 3D Facial Morphometry: Insights from Cri-du-Chat Syndrome 93%
- Assessing performance of pathogenicity predictors using clinically-relevant variant datasets 93%
- De novo coding variants in the AGO1 gene cause a neurodevelopmental disorder with intellectual disability 92%
Similar papers in this journal
- Bi-allelic loss-of-function variants in PPFIBP1 cause a neurodevelopmental disorder with microcephaly, epilepsy and periventricular calcifications 92%
- GA4GH Phenopacket-Driven Characterization of Genotype-Phenotype Correlations in Mendelian Disorders 92%
- Consensus guidelines for eligibility assessment of pathogenic variants to antisense oligonucleotide treatments 92%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.