Digenic variant interpretation with hypothesis-driven explainable AI
De Paoli, F.; Nicora, G.; Berardelli, S.; Gazzo, A. M.; Bellazzi, R.; Magni, P.; Rizzo, E.; Limongelli, I.; Zucca, S.
Show abstract
MotivationThe digenic inheritance hypothesis holds the potential to enhance diagnostic yield in rare diseases. Computational approaches capable of accurately interpreting and prioritizing digenic combinations based on the probands phenotypic profiles and familial information can provide valuable assistance to clinicians during the diagnostic process. ResultsWe have developed diVas, a hypothesis-driven machine learning approach that can effectively interpret genomic variants across different gene pairs. DiVas demonstrates strong performance both in classifying and prioritizing causative pairs, consistently placing them within the top positions across 11 real cases (achieving 73% sensitivity and a median ranking of 3). Additionally, diVas exploits Explainable Artificial Intelligence (XAI) to dissect the digenic disease mechanism for predicted positive pairs. Availability and ImplementationPrediction results of the diVas method on a high-confidence, comprehensive, manually curated dataset of known digenic combinations are available at oliver.engenome.com.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
- MetaRNN: Differentiating Rare Pathogenic and Rare Benign Missense SNVs and InDels Using Deep Learning 96%
- Genome-wide prediction of pathogenic gain- and loss-of-function variants from ensemble learning of diverse feature set 95%
- Mendelian gene identification through mouse embryo viability screening 95%
Similar papers in this journal
- Genome Alert!: a standardized procedure for genomic variant reinterpretation and automated genotype-phenotype reassessment in clinical routine 96%
- The Importance of Automation in Genetic Diagnosis: Lessons from Analyzing an Inherited Retinal Degeneration Cohort with the Mendelian Analysis Toolkit (MATK) 96%
- Discovering Monogenic Patients with a Confirmed Molecular Diagnosis in Millions of Clinical Notes with MonoMiner 95%
Similar papers in this journal
- DeMAG predicts the effects of variants in clinically actionable genes by integrating structural and evolutionary epistatic features 95%
- Deep representation learning for clustering longitudinal survival data from electronic health records 93%
- Identification of putative causal loci in whole-genome sequencing data via knockoff statistics 93%
Similar papers in this journal
- UK-Biobank Whole Exome Sequence Binary Phenome Analysis with Robust Region-based Rare Variant Test 94%
- Genetic association studies using disease liabilities from deep neural networks 94%
- The landscape of autosomal-recessive pathogenic variants in European populations reveals phenotype-specific effects 94%
"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.