Guy Karlebach

This paper introduces a genomic framework to prioritize mouse strains for preclinical disease modeling, showing high sensitivity in predicting strain vulnerability to diseases.

This paper introduces an optimal algorithm for inferring asynchronous Boolean networks from experimental data, balancing model fit, size, and computational efficiency.

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

The paper develops an algorithm to compute minimal Boolean models of gene regulatory networks from single-cell RNA-Seq data, demonstrating its effectiveness on a public dataset.

The paper introduces a degree-aware edge sampling method to reduce performance inflation in biomedical graph representation learning caused by imbalanced edge degree distributions.

The paper introduces isopret, an algorithm for predicting isoform-specific functions using RNA-seq data, and shows that expression and splicing regulate distinct biological functions.

The paper investigates differential alternative splicing in cells infected by betacoronaviruses and finds characteristic patterns linked to viral load and gene function.

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.