We offer advanced multi-omics analysis and complex data integration services designed to address biological, clinical, and industrial questions from a global perspective. We work with all types of omics data, tailoring each analysis to the specific needs of each project and the required depth.
We analyze and combine information from genomics, transcriptomics, epigenomics, proteomics, metabolomics, microbiome, as well as clinical, phenotypic, environmental, and experimental data. Our approaches enable both the individual analysis of each data layer and their joint integration, with the goal of extracting actionable knowledge and obtaining a systemic view of biological processes.
We design customized, modular analysis workflows that integrate advanced statistical models and machine learning techniques, scalable from exploratory studies to complex models aimed at prediction, stratification, classification, or decision support. In all cases, we seek a balance between statistical rigor, biological interpretability, and predictive performance, adapting the methodologies to the scientific, clinical, or industrial context of each study.
Integrative network analysis connects omics, clinical, and metadata layers to reveal how variables influence each other across biological systems. We build statistical and machine learning models tailored to your needs, prioritizing interpretability when understanding mechanisms matters, and predictive power when accuracy drives decisions.
If you're looking to:
- Discover causal relationships and generate testable hypotheses -> Bayesian networks provide transparent, mechanistic insights.
- Maximize predictive performance for patient stratification or diagnostics -> Neural networks handle complexity at scale.
- Map functional modules, identify key regulators, or benchmark multiple methods -> Graph-based and hybrid approaches offer flexibility and depth.
Integrative Bayesian Networks
When causality and interpretability are non-negotiable.
Bayesian networks excel at modeling complex dependencies while maintaining biological plausibility. We use them to infer causal relationships, identify disease drivers, and integrate multi-omics data with clinical outcomes. Each edge in the network represents a statistical dependency you can interrogate, making results explainable to researchers and clinicians alike.
Neural Networks
When predictive accuracy unlocks clinical or commercial value.
Deep learning architectures handle high-dimensional data and non-linear interactions that traditional methods miss. We design task-specific neural networks, from autoencoders for dimensionality reduction to graph neural networks for molecular property prediction, with rigorous validation to prevent overfitting and ensure generalizability.
Other Approaches
Hybrid, ensemble, and graph-based strategies for complex challenges.
Not every problem fits a single framework. We combine Bayesian priors with neural architectures, leverage graph-based methods for pathway analysis, and deploy ensemble models that balance multiple objectives. Our toolkit includes random forests, support vector machines, variational autoencoders, and custom algorithms tailored to your data structure.
Graph Enrichment and Network Insights
Graph enrichment, centrality, and community analyses reveal hidden structures in your biological networks. We identify key regulatory hubs through centrality metrics, detect functional modules via community detection, and perform pathway enrichment to connect molecular patterns to biological processes. These methods transform complex interaction networks into interpretable functional units.