Discovering causal paths to diabetic nephropathy by combining computable biomedical knowledge with graph mining algorithms

Purpose The present study aimed to discover the causal paths from diabetes to diabetic nephropathy (DN) from scientific texts by combining computable biomedical knowledge in SemMedDB with graph mining algorithms. Methods A total of 12,662 triples were included in this study, containing 3,374 unique concepts and 44 semantic relations. We built a directed knowledge graph (KG) and then pruned it to a causal graph via word2vec word embeddings, semantic relations, and path length. Filtering thresholds were adjusted multiple times to find optimal causal paths and third variables. The paths and variables were validated by a nephrologist. Results A path from diabetes to DN was sorted out, illustrating the key inducer of pathogenesis and two of the most noteworthy clinical outcomes. With the decrease of the directed causal score (S_di) from Quantile 95% to Quantile 75%, paths from diabetes to DN increased and third explicable variables and edges emerged additionally. Conclusions This study developed an efficient causal path discovery approach to sort out the predominant path from pathogenesis to the manifestation of complex disorders.