Automating Knowledge-Driven Model Recommendation: Methodology, Evaluation, and Key Challenges

2023/01/26 by Adam Butchy, Butchy, Adam A., Cheryl A. Telmer +3
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Biomedical Text Mining and Ontologies #FOS: Biological sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN)

paper · pdf · doi:10.48550/arxiv.2301.11397

openalex publication_date 2023/01/26 · openalex created_date 2023/01/31 · openalex updated_date 2026/07/28

Abstract

There is significant interest in using existing repositories of biological entities, relationships, and models to automate biological model assembly and extension. Current methods aggregate human-curated biological information into executable, simulatable models, but these models do not resemble human curated models and do not recapitulate experimental results. Here, we outline the process of automated model assembly and extension, while demonstrating it on both synthetic models and human-curated models of biological signaling networks. We begin with an iterative, greedy, and combinatoric approach to automated assembly and demonstrate the key difficulties inherent to contextless assembly. We publicly release the software used in this paper to enable further exploration of this problem.

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