Irina I Mohorianu

Crecerelle integrates gene expression and alternative splicing to improve cell-type annotation and uncover regulatory pathways in single-cell transcriptomics.

SMEW provides a code-free, interactive platform for analyzing spatial metabolomics data across multiple scales and conditions to identify metabolic patterns and biological pathways.

This study compares acellular and red cell-based subnormothermic machine perfusion in human kidneys, finding no significant differences in transcriptional profiles or injury biomarkers between the two methods.

The paper introduces MAGPIE, a framework for integrating spatial transcriptomics, metabolomics, and tissue morphology data, demonstrating its application in studying drug-induced pulmonary fibrosis.

The paper introduces Flufftail, a framework for fuzzy clustering in single-cell data, to infer gene regulatory network dynamics by leveraging membership probabilities and regulatory hub genes.

This study develops a multi-omics framework to identify biomarkers of inhaled drug-induced lung toxicity using spatial transcriptomics, proteomics, and histopathological data.

This study identifies increased cholesterol synthesis in PMS patient-derived neural progenitor cells as a driver of neurotoxicity, which can be mitigated by simvastatin.

This paper develops a 3D multicellular model of NAFLD using hiPSC-derived liver cells to study disease mechanisms and interactions.

bulkAnalyseR provides an interactive pipeline for analyzing and sharing bulk multi-modal sequencing data, offering quality checks, differential expression, and regulatory network analysis.

This paper investigates how splitting sequencing samples across lanes affects results in bulk and single-cell sequencing, showing reduced detail and potential bias in analyses.

This study shows RBPMS induces contractile splicing patterns and alters phenotypic behavior in human embryonic stem cell-derived vascular smooth muscle cells.

This study maps in vitro megakaryocyte progenitor states to their in vivo counterparts and identifies surface markers for their purification.

ClustAssess evaluates clustering robustness in single-cell data by analyzing algorithmic and technical impacts on cell-type identification.

noisyR reduces technical noise in sequencing data to enhance biological signal detection and improve downstream analysis consistency.

feamiR uses genetic algorithms to select features for predicting miRNA-mRNA interactions, improving accuracy by analyzing seed, compensatory, and flanking regions.

PAREameters infers miRNA-mRNA targeting rules from RNA sequencing data, showing increased sensitivity when using data-inferred criteria across multiple plant species.

The paper investigates how technical noise affects bulk RNA-seq differential gene expression analysis and proposes noise-adjustment methods to improve consistency across tools.

The paper evaluates and improves RNA-seq data quality checks and differential expression analysis across multi-level designs in D. melanogaster and H. sapiens.