Camila M. Lopes‐Ramos

The paper identifies sex-biased regulation of cancer genes in normal tissues, linking these differences to sex-specific cancer incidence patterns.

This study identifies aging-related changes in gene regulatory networks linked to LUAD risk, prognosis, and therapy response using multi-omics data from GTEx and TCGA.

The paper identifies sex differences in lung adenocarcinoma by integrating multi-omics data to discover sex-biased signaling pathways and potential sex-specific therapies.

The paper develops a Nextflow workflow for reproducibly inferring regulatory networks from TCGA data, showing its application to colon cancer subtypes.

This study investigates how loss of the Y chromosome in lung adenocarcinoma affects the tumor immune environment and response to immunotherapy.

BONOBO infers sample-specific co-expression networks to capture individual heterogeneity in gene regulation, outperforming existing methods in yeast, breast cancer, and thyroid tissue analyses.

The paper introduces SNAIL, a normalization method that reduces false-positive co-expression associations in RNA-sequencing data, improving downstream network analysis.

The paper introduces netZoo, an open-source software suite for analyzing biological networks, demonstrating its use on cancer cell line data to identify drug resistance and cancer development factors.

GRAND is a database of gene regulatory network models across human conditions, enabling comparison between biological states and drug response prediction.

The paper identifies PD1 signaling regulation as a prognostic marker in glioblastoma using gene regulatory network analysis across two cohorts.

This paper presents a theoretical framework for PANDA, a gene regulatory network inference method, and shows it outperforms state-of-the-art methods in predicting transcription factor binding.

The paper identifies sex-linked differences in colon cancer drug metabolism via gene regulatory network analysis, showing improved survival in females with higher pathway targeting.

The paper develops a preprocessing and normalization pipeline for large-scale RNA-Seq data, addressing batch and tissue effects in heterogeneous datasets.

This study analyzes gene regulation across 38 tissues to show that transcription factor-target connections are more tissue-specific than genes, and that tissue-specific genes are not highly targeted in their networks but occupy bottleneck positions.

The paper compares gene expression and regulatory patterns between cell lines and their source tissues, finding reduced expression levels and altered transcription factor regulation in cell lines.

The paper uses a network-based approach to analyze eQTLs across 13 tissues, identifying cis- and trans-eQTLs and linking SNPs to biological processes and disease associations.

The paper identifies sexual dimorphism in gene expression and regulatory networks across 31 human tissues using GTEx data, showing that many genes and networks are sex-specific.