Rémy Nicolle

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c-MYC controls more than 15% of genes responsible for proliferation, differentiation, and cellular metabolism in pancreatic as well as other cancers making this transcription factor a prime target for treating patients. The transcriptome of 55 patient-derived xenografts show that 30% of them share an exacerbated expression profile of MYC transcriptional(More)
UNLABELLED We introduce Pepper (Protein complex Expansion using Protein-Protein intERactions), a Cytoscape app designed to identify protein complexes as densely connected subnetworks from seed lists of proteins derived from proteomic studies. Pepper identifies connected subgraph by using multi-objective optimization involving two functions: (i) the(More)
Amplification of the 8p11-12 chromosomal region is a common genetic event in many epithelial cancers. In breast cancer, several genes within this region have been shown to display oncogenic activity. Among these genes, the enzyme-encoding genes, PPAPDC1B and WHSC1L1, have been identified as potential therapeutic targets. We investigated whether PPAPDC1B and(More)
Preclinical models based on patient-derived xenografts have remarkable specificity in distinguishing transformed human tumor cells from non-transformed murine stromal cells computationally. We obtained 29 pancreatic ductal adenocarcinoma (PDAC) xenografts from either resectable or non-resectable patients (surgery and endoscopic ultrasound-guided fine-needle(More)
UNLABELLED CoRegNet is an R/Bioconductor package to analyze large-scale transcriptomic data by highlighting sets of co-regulators. Based on a transcriptomic dataset, CoRegNet can be used to: reconstruct a large-scale co-regulatory network, integrate regulation evidences such as transcription factor binding sites and ChIP data, estimate sample-specific(More)
In tumoral cells, gene regulation mechanisms are severely altered. Genes that do not react normally to their regulators' activity can provide explanations for the tumoral behavior, and be characteristic of cancer subtypes. We thus propose a statistical methodology to identify the misregulated genes given a reference network and gene expression data. Our(More)
Classical approaches to analyze transcriptomic data usually produce average classification models that have very low reproducibility. In this work, genome wide gene expression is considered through the activity of large regulatory networks. We introduce a new measure of regulatory influence based on the variations of expression of genes in a large inferred(More)
Reconstruction of large scale gene regulatory networks (GRNs in the following) is an important step for understanding the complex regulatory mechanisms within the cell. Many modeling approaches have been introduced to find the causal relationship between genes using expression data. However, they have been suffering from high dimensionality-large number of(More)
Conventional approaches to predict transcriptional regulatory interactions usually rely on the definition of a shared motif sequence on the target genes of a transcription factor (TF). These efforts have been frustrated by the limited availability and accuracy of TF binding site motifs, usually represented as position-specific scoring matrices, which may(More)