disease-trajectories

Extract disease trajectory relationships from DisTraj JSON by running dt_extract_edges.py to parse phase_dict structures, normalize disease_a_id and disease_b_id pairs, and output a normalized edge list for mapping.

Convert DisTraj outputs into comorbidity YAML signals by parsing DT JSON/TSV artifacts, extracting directed disease pairs, applying sex or significance filters, and mapping the normalized statistical fields into dismech comorbidity YAML.

Filter comorbidity trajectory pairs by sex or statistical significance during edge extraction to focus on relevant directed disease relationships before mapping them into the dismech knowledge base YAML format.

No external dependencies are required to parse DT JSON and map disease trajectory signals; the Skill bundles all necessary Python scripts, including dt_extract_edges.py, to handle normalization, edge parsing, and YAML mapping rules.

The disease trajectory parser supports DT JSON phase_dict structures and TSV edge lists, normalizing them into disease_a_id, disease_b_id, directionality, and statistical fields for downstream comorbidity mapping.