Our Approach
We capture multi-modal data across human biology, including:
- Genetics and gene expression
- Proteins, metabolites, and lipids
- Immune system signatures and antibodies
- Clinical labs and health traits
- Longitudinal behavioral and phenotypic signals
This enables a more complete view of health and disease than any single modality.
QA/QC and process optimization are designed to reduce experimental error and improve signal reliability.
Standardized collection, assay alignment, and bias-aware refinement aim to ensure data quality prior to downstream analysis.
We transform heterogeneous datasets into a comparable framework by standardizing measurements, correcting for bias, and aligning cohorts across studies.
This enables more consistent cross-study and longitudinal comparisons.
Analytical approaches are used to identify patterns, associations, and potential predictive signals across datasets.
These outputs support biological interpretation and hypothesis generation.
At the individual level, Alden develops computational models representing aspects of biological state, risk, and potential trajectories based on available data.
At the population level, similar approaches can support research and exploratory analysis.
These models are iterative and improve as additional data becomes available.
Insights derived at the individual level—including longitudinal changes, interventions, and observed outcomes — are incorporated back into the broader dataset and modeling framework.
This enables refinement of population-level understanding over time, supporting more robust interpretation, improved model performance, and the identification of patterns that may not be observable in isolated datasets.