Daniel Domingo‐Fernándéz

TIMS-Bench evaluates three TIMS feature-finding tools using public and benchmark datasets, identifying strengths and weaknesses in feature detection and annotation.

OmniPath integrates molecular knowledge from 168 resources to support multi-omics analysis, offering curated data, predictions, and tools for causal modeling across bulk, single-cell, and spatial data.

The paper investigates how the H1/H1 risk allele at the MAPT locus increases susceptibility to mild chronic oxidative stress in iNeurons and identifies FDA-approved compounds that rescue this vulnerability.

The paper estimates the size of plant chemical space using metabolomics and literature data, suggesting it may span millions of unique compounds, most of which remain unexplored.

This paper introduces MultiGML, a multimodal graph machine learning approach that predicts adverse drug events by integrating diverse data sources and provides explainable biological insights.

The paper examines the relationship between compound-target interactions and their transcriptomic effects, finding limited correlation but potential for pathway-based analysis.

This paper evaluates how different thresholding methods affect ATN profiles across AD cohorts, finding high variability and limited generalizability of results.

This study identifies shared genetic and biological mechanisms between schizophrenia, bipolar disorder, and type 2 diabetes mellitus using integrative analysis of gene expression datasets.

The paper introduces the Bioregistry, an open metaregistry that integrates and expands existing biomedical entity registries to improve interoperability and data reuse.

This work analyzes transcriptomic data across multiple biological contexts to identify unique and shared gene expression patterns using co-expression networks and pathway analysis.

RPath prioritizes drugs for diseases by analyzing causal paths in knowledge graphs using drug-perturbed and disease-specific transcriptomic signatures, outperforming existing methods.

The paper introduces STonKGs, a Transformer model trained on biomedical text and knowledge graphs, which outperforms single-modality models in classification tasks.

DecoPath is a web tool that helps interpret pathway enrichment analysis results by comparing across databases and methods, highlighting consensus and discrepancies.

This paper investigates transcriptomic signatures across diseases by integrating co-expression networks and pathway knowledge to identify disease mechanisms.

CLEP generates patient representations by integrating prior biological knowledge with patient data, improving classification performance compared to using raw transcriptomics data.

The paper introduces MultiPaths, a Python framework for analyzing multi-layer biological networks using diffusion algorithms, showing improved predictive performance through integrated multi-omics data.

This paper uses predictive machine learning models to simulate drug responses by calibrating patient-specific pathway signatures, identifying potential drugs and mechanisms for three cancers.

Drug2ways predicts drug candidates by analyzing causal paths in biological networks for disease treatment and drug repositioning.

The paper presents a drug-target-mechanism graph to identify synergistic drug combinations for SARS-CoV-2 treatment, validated through experimental testing.

The paper constructs a cause-and-effect knowledge graph of COVID-19 pathophysiology from scientific literature and provides it as a public resource.

The paper investigates the potential link between heme levels and COVID-19 pathogenesis by analyzing protein interactions and common pathways.

The paper develops an ontology and knowledge graph to model heme biology and investigates its role in Toll-like receptor signaling.

This study benchmarks the impact of pathway database choice on enrichment analysis and predictive modeling, showing that using multiple or integrative databases improves consistency and performance.

PathMe harmonizes three pathway databases into a unified schema to enable comprehensive exploration of pathway crosstalk and consensus.

The paper introduces Bio2BEL, a set of Python packages that convert multi-modal biological data into Biological Expression Language for integration and analysis.

BioKEEN provides a tool for learning and evaluating biological knowledge graph embeddings with an interactive interface and a case study on pathway crosstalk prediction.

The paper develops workflows to re-curate and enrich knowledge graphs encoded in Biological Expression Language using automated and manual methods.

The PTSD Biomarker Database compiles over 900 PTSD biomarkers from 109 studies to enable exploration and comparison of biomarker findings through interactive visualizations.

ComPath curates pathway mappings between databases and provides tools for exploring and analyzing pathway knowledge, demonstrating its utility in identifying biological insights in Parkinson's disease.

The paper identifies shared genetic and mechanistic bases between epilepsy and its comorbidities using literature mining and knowledge assembly.