Daniel Daniš

The paper identifies pathogenic variants in 15.8% of CP patients and finds only 16 of 60 genes have significant CP association.

Ontologizer 3 provides a desktop tool for GO enrichment analysis using frequentist and Bayesian methods, showing higher precision with the Bayesian approach on simulated data.

This study benchmarks large language models against a traditional diagnostic tool for genetic disease diagnosis using 5,213 case reports, finding LLMs lag behind in accuracy.

The paper introduces GPSEA, a tool using GA4GH Phenopackets to analyze genotype-phenotype correlations in Mendelian disorders, identifying 225 significant associations across 85 cohorts.

The paper compares semantic similarity algorithms for differential diagnosis and clustering using HPO, finding Phenomizer performs best for ranking and non-normalized algorithms for clustering.

RareLink enables interoperability between REDCap and global health standards by exporting data to Phenopackets and FHIR, improving rare disease research scalability and data reuse.

The paper shows that alternative splicing is coupled to gene expression in a subset of variably expressed genes across multiple tissues.

The paper presents a large-scale corpus of standardized phenopackets for genomic diagnostics and discovery, curated from literature and useful for software testing and genotype-phenotype analysis.

The paper evaluates splicing prediction methods on 56 clinically validated variants, finding some methods match or exceed SpliceAI's performance but highlighting ongoing challenges in accuracy.

The paper introduces the GA4GH Phenopacket schema to standardize the exchange of phenotypic data for precision medicine applications.

SQUIRLS prioritizes splice variants by calculating interpretable features and achieving higher accuracy and speed than existing methods in simulated exomes.

SvAnna predicts pathogenicity of structural variants in long-read sequencing data by integrating transcript and regulatory annotations with clinical phenotype data, achieving high accuracy in prioritizing pathogenic variants.

The paper identifies sex-biased differential alternative splicing across multiple human tissues and links it to gene function and estrogen response.

The paper introduces LIRICAL, a likelihood ratio-based framework for genomic diagnostics that improves accuracy and interpretability in phenotype-driven rare disease diagnosis.

This paper introduces HBA-DEALS, a method that simultaneously identifies differential expression and splicing, showing genes can have differential expression, splicing, both, or neither.

parSMURF is a parallel machine learning tool for detecting pathogenic variants in genomic data, achieving state-of-the-art results with an 80x speed-up over sequential versions.

The paper semantically integrates LOINC-encoded laboratory data with the Human Phenotype Ontology to identify asthma biomarkers from EHR data.