Tomer Kalisky C

The paper analyzes bulk RNA-seq data from a kidney IRI mouse experiment to identify temporal phases and alternatively spliced genes, revealing potential splicing regulators involved in injury and repair.

The paper introduces robustness measures to evaluate cell-type classification reliability in single-cell RNA-seq datasets of kidney organoids and finds high robustness in most cell types except immature ones.

This study uses unsupervised machine learning to identify temporal gene expression components in HeLa cells after cell cycle release, revealing oscillatory and transient patterns linked to cell cycle phases and cancer-related processes.

This study uses RE:IN to characterize GRN ensembles in a mouse kidney injury model, identifying four GRN families and two substructures that distinguish them.

The paper analyzes RNAseq data to identify alternative splicing patterns associated with tumor thrombus formation in clear-cell renal cell carcinoma.

This study characterizes alternative splicing heterogeneity in high-risk Wilms tumors using RNAseq data, identifying splicing patterns linked to kidney development and EMT.

The paper introduces a python-based toolkit for Networks Based Clustering (NBC) and demonstrates its effectiveness in analyzing large-scale RNA-seq datasets.

The paper uses single-cell RNA sequencing to study mRNA splice isoform switching during mouse kidney development and identifies potential splicing regulators.

The paper identifies splice isoform switching during human kidney development and links it to Wilms tumor by analyzing RNA sequencing data from fetal kidney stages and tumor models.