Miguel Angel Andrade-Navarro

The paper introduces MAPPIE, a method to map human protein-protein interactions into a 2D space for functional discovery, outperforming baselines in sparse interaction regions.

GDDN connects gene-sets to diseases and therapeutics to prioritize drugs, enabling functional analysis and personalized medicine.

The paper introduces MIRNAPEX, a framework that accounts for alternative polyadenylation to better understand miRNA differential expression by integrating target-gene expression and 3'UTR isoform usage.

The paper uses a multi-property fine-tuned language model to predict lipid retention times from peptide data, improving accuracy in data-sparse scenarios.

The paper develops a computational framework to predict cooperative and competitive interactions among protein triplets in the human interactome using hyperbolic embeddings and machine learning.

This study uses machine learning to predict miRNA expression and identify genetic regulators, revealing miRNA-gene regulatory relationships and pathway-specific associations.

The paper improves seqQscorer by integrating the ENCODE blocklist to enhance ChIP-seq quality assessment accuracy and efficiency.

The paper uses MD simulations to study helical stability in polyXY sequences at IDR boundaries, finding they form dynamic helices that may aid in protein flexibility.

The paper reduces CRISPR dark matter by analyzing spacers in antibiotic-resistant bacterial genomes and links CRISPR-Cas systems to membrane proteins involved in phage defense.

The paper investigates harmonin expression and localization in human retinas to understand USH1C pathophysiology and potential gene therapy approaches.

SIMPA imputes missing regions in scChIP-seq data using machine learning models trained on bulk data, improving clustering and pathway gene identification while enabling model interpretation.

This study identifies Yap1 as a regulator of adult neural stem cell activation, showing that its gain-of-function mutant promotes cell cycle entry and aberrant differentiation while wild-type Yap1 does not.

The paper uses machine learning to identify functional residues in odorant receptors across insect species, revealing conservation patterns linked to social behavior.

This paper develops machine learning models for automated quality control of next generation sequencing data, validated on internal and external datasets.

The paper develops a method to identify cell-specific variable regions in ChIP-seq data by integrating replicated experiments, finding regions with variable occupancy that may have functional significance.

The paper benchmarks 10 scATAC-seq analysis methods on synthetic and real datasets, finding SnapATAC, Cusanovich2018, and cisTopic perform best in separating cell populations across varying coverage and noise levels.

The paper introduces 7C, a method using ChIP-seq data to predict chromatin loops with high resolution and applies it to identify loop-associated genes and candidate factors.

TAF-ChIP enables high-quality ChIP-Seq results from low cell numbers, matching conventional ChIP-Seq datasets in epigenetic profiles.

The paper investigates the evolutionary conservation of TADs and finds that TAD boundaries are enriched with rearrangement breakpoints, while TADs show conserved gene expression patterns.

This paper embeds the human protein interaction network in hyperbolic space to reveal latent geometric properties that correlate with biological features and predict protein interactions.

The paper evaluates engineered cell identity using CellScore, finding that iPS cells achieved high scores while other engineered cells had lower scores.

The paper constructs a reliable and unbiased human protein network using a disparity filter to address biases in existing networks.