Michal Linial B

PWAS detects protein-coding genes associated with phenotypes by analyzing proteomic context and using machine learning, showing improved detection of causal genes and new associations.

BIRD identifies cell doublets using biallelic expression ratios from single-cell RNA-seq data, achieving high accuracy in simulated and real datasets.

The paper uses a stochastic framework to analyze gene sensitivity to miRNA regulation in HeLa cells, identifying gene sets particularly sensitive to miRNA overexpression.

The paper uses molecular dynamics simulations to compare SARS-CoV-2 and other coronaviruses' interactions with the human ACE2 receptor, revealing distinct binding interfaces and dynamics.

The paper uses a probabilistic framework to study miRNA regulation, finding that translation machinery is insensitive to miRNA manipulations across cell types.

The paper reports results from the CAFA3 challenge, showing improved computational protein function prediction and new annotations for over 1000 genes through experimental screens in C. albicans, P. aureginosa, and Drosophila.

The paper analyzes allele-specific expression (ASE) variants across human samples, identifying their genomic features and associations with immune-related phenotypes and diseases.

The paper identifies X-chromosome escapees using single-cell RNA-Seq in fibroblasts and lymphoblasts, finding 24 and 49 candidates, respectively, with 66% previously reported and 11 being lncRNAs.

The paper introduces a machine learning framework to identify cancer driver genes by analyzing the functional impact of somatic mutations, finding 593 genes with significant harmful mutation bias.

This study identifies cancer types using lowly expressed microRNAs from TCGA data, achieving 91% recall after data discretization.

The paper identifies human genes that escape X-chromosome inactivation using single-cell RNA-Seq and allelic expression analysis, finding 25-31 escapee genes with high confidence.