Nency Bansal

This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).

This work develops a method to infer a Corpus-Wide Causal Score (CWCS) for a gene-disease pair by integrating network-based causal signals in a gene regulatory network (CWCS-Net) with corpus-wide literature evidence from PubMed abstracts quantified by a newly developed Truth Discovery algorithm (CWCS-TD), achieving a causal class F1 score of 0.600 across ten diseases using OMIM as an external expert-curated reference, outperforming GPT-4o (0.505) and MMed-Llama 3 (0.522).