Anaı̈s Baudot

This study evaluates how data leakage affects KGE models' performance in biomedical link prediction and finds that current benchmarks may overestimate generalization.

PhenoXtract extracts phenotypes from clinical descriptions using a hybrid approach of Large Language Models and Knowledge Graph embeddings, achieving high recall and precision.

The paper introduces minkiPy, a geometric framework for analyzing spatial gene expression patterns using Minkowski functionals and tensors, and applies it to spatial transcriptomics datasets.

EV-Net is a computational tool that adapts NicheNet to analyze EV cargo effects on recipient tissues, enabling prioritization of regulatory molecules from proteomics and RNA-seq data.

Pylluminator provides a scalable Python toolkit for analyzing DNA methylation data from Illumina arrays, offering preprocessing, quality control, and differential methylation analysis.

MOTL improves multi-omics data factorization for small datasets by transferring knowledge from large datasets, enhancing cancer subtype identification in glioblastoma samples.

The paper develops a network-based method to identify novel phenotypes for rare diseases by integrating clinical data with prior knowledge from Orphanet and HPO.

The paper refines a human heart atlas using multi-modal data from the first trimester, identifying novel cell types and spatial features in cardiac development.

PSAP-genomic-regions improves variant prioritization in whole genome sequencing by incorporating functional genomic regions, enhancing detection of both coding and non-coding pathogenic variants in rare disease diagnosis.

The paper analyzes genes mutated in muscular dystrophies to identify functional patterns linked to distal or proximal muscle weakness distribution.

The paper demonstrates the versatility of MultiXrank, a Random Walk with Restart algorithm on multilayer networks, for gene prioritization, gene-disease association prediction, and disease signature computation.

This study applies collaborative network analysis to Huntington's disease transcriptomics data to identify enriched pathways and generate hypotheses about disease mechanisms.

The paper integrates multi-omics data from CAKUT patients to identify collagen's role in disease development and provides containerized workflows for analysis.

This study shows that mouse embryonic stem cell-derived gastruloids can differentiate into both cardiac and skeletal muscle lineages, revealing insights into cardiopharyngeal mesoderm specification.

The paper presents a KNIME workflow for scRNA-seq analysis that is customizable, interactive, and automated, enabling users without programming expertise to perform key analysis steps.

MOGAMUN identifies active modules in multiplex biological networks by optimizing interaction density and node scores, outperforming existing methods in interpretability and handling multiplex data.

The paper investigates SMCHD1's role in two genetic syndromes, showing it regulates chromatin and gene expression through DNA methylation and chromatin organization.

This study benchmarks nine joint multi-omics dimensionality reduction methods using simulated and real cancer data, finding intNMF excels in clustering and MCIA shows consistent performance across contexts.

This study profiles the proteomes and phosphoproteomes of four prostate cell lines to identify differentially expressed proteins and phosphosites associated with resistance and cancer progression.

The paper uses matrix factorization to analyze molecular mechanisms behind the inverse comorbidity between Alzheimer’s disease and lung cancer, identifying new candidate factors and subtype-specific mechanisms.

The paper compares gene expression profiles of ASD and 22 cancer types, finding shared and opposing deregulations in specific cancers and common biological pathways.

The paper extends the random walk with restart algorithm to multiplex and heterogeneous biological networks to improve disease gene prediction.