Aristotle

Aristotle performs stratified causal discovery on high-dimensional genomics, transcriptomics, proteomics, and metabolomics data to identify subgroup-specific causal relationships.

Key Features:

• High-dimensional omics support: Operates on genomics, transcriptomics, proteomics, and metabolomics datasets to address challenges of high dimensionality.
• Integration of biological knowledge: Incorporates existing biological knowledge to inform and make strata biologically relevant.
• Patient stratification: Employs advanced stratification methods to identify hidden subgroups within populations.
• Stratum-specific causal inference: Applies a quasi-experimental design within each identified stratum to uncover subgroup-specific potential causes.
• Performance evaluation: Demonstrated improved causal discovery performance on synthetic data under varied conditions.
• Real-world validation: Validated predictions on a dataset concerning Anthracycline Cardiotoxicity against established literature.

Scientific Applications:

• Stratified causal discovery: Detects causal relationships that are specific to subpopulations within omics cohorts.
• Personalized medicine research: Identifies patient subgroups that may exhibit distinct treatment responses or risks.
• Disease mechanism elucidation: Supports investigation of subgroup-specific biological mechanisms underlying phenotypes and disease states.
• Prioritization of hypotheses: Generates stratum-specific causal hypotheses for targeted experimental follow-up.

Methodology:

Integrates biological knowledge to inform stratification, applies advanced patient stratification methods to identify hidden strata, performs quasi-experimental design-based causal inference within each stratum, and evaluates performance on synthetic data with validation on an Anthracycline Cardiotoxicity dataset.