GBM model refinement with literature curation, rule-based NLP, and LLMs
Arazkhani, N.; Luo, H.; Tang, D.; Cochran, B.; Miskov-Zivanov, N.
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In this work, our goal was twofold: (1) improve an existing glioblastoma multiforme (GBM) executable mechanistic model and (2) evaluate the effectiveness traditional natural language processing (NLP) pipeline and the generative AI approach in the process of model improvement. We used a suite of graph metrics and tools for interaction filtering and classification to collect data and conduct the analysis. Our results suggest that a more comprehensive literature search is necessary to collect enough information through automated paper retrieval and interaction extraction. Additionally, we found that graph metrics present a promising approach for model refinement, as they can provide useful insights and guidance when selecting new information to be added to a mechanistic model.
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