María de la Iglesia-Vayá

The paper analyzes single-cell data from multiple sclerosis patients to identify sex-specific molecular differences in brain and immune cells across disease progression.

MetaOmixTools provides a web-based platform for meta-analysis of ranked features and functional enrichment, enabling integration of multi-study omics data to identify conserved biological insights.

The paper identifies shared transcriptomic features between melanoma brain metastases and neurodegenerative diseases, highlighting key genes and providing an open-source tool for analysis.

MetaFun performs functional meta-analysis of transcriptomic datasets to uncover sex differences across multiple omics studies.

This paper identifies sex-based transcriptomic differences in schizophrenia patients across specific brain regions through meta-analysis of bulk RNA-sequencing studies.

This paper identifies sex-specific genes, transcription factors, and pathways in Alzheimer's disease using meta-analyses of transcriptomic data from the cerebral cortex and hippocampus.

This paper identifies sex-specific miRNA expression differences in Alzheimer's disease patients through meta-analysis of blood and brain samples, revealing distinct biological process associations in each sex.

This study identifies sex-based molecular differences in multiple sclerosis through transcriptomic meta-analysis, revealing tissue-specific gene expression patterns and functional immune pathway alterations.

This study identifies sex-based differences in Parkinson's disease transcriptomics through meta-analysis of 267 samples across three brain regions, revealing sex-specific gene expression and pathway alterations.

This paper evaluates sex bias in housekeeping gene expression in adipose tissue using transcriptomics data and identifies sex-unbiased housekeeping genes suitable for use as internal controls.

This paper performs a meta-analysis of transcriptomic studies to identify sex-based differences in molecular mechanisms during the progression from NAFL to NASH.

This study performs a meta-analysis of transcriptomic data to identify sex-based differences in functional signatures of non-small-cell lung cancer, finding enrichment in immune response and purinergic signaling in females.

This paper introduces a new methodology for processing resting-state fMRI using EEG electrode positions as seeds and analyzes the resulting functional connectivity networks.