Recipes for Translating Big Data Machine Reading to Executable Cellular\n Signaling Models

2017/06/13 by Khaled Sayed, Sayed, Khaled, Cheryl A. Telmer +5
Biochemistry, Genetics and Molecular Biology · #Bioinformatics and Genomic Networks #Cell Image Analysis Techniques #FOS: Biological sciences #Gene Regulatory Network Analysis #Molecular Networks (q-bio.MN)

paper · pdf · doi:10.48550/arxiv.1706.04117

openalex publication_date 2017/06/13 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

With the tremendous increase in the amount of biological literature,\ndeveloping automated methods for extracting big data from papers, building\nmodels and explaining big mechanisms becomes a necessity. We describe here our\napproach to translating machine reading outputs, obtained by reading bio-\nlogical signaling literature, to discrete models of cellular networks. We use\nout- puts from three different reading engines, and describe our approach to\ntranslating their different features, using examples from reading cancer\nliterature. We also outline several issues that still arise when assembling\ncellular network models from state-of-the-art reading engines. Finally, we\nillustrate the details of our approach with a case study in pancreatic cancer.\n

Related

• Methods to Expand Cell Signaling Models using Automated Reading and Model Checking
• OmniCellTOSG: The First Cell Text-Omic Signaling Graphs Dataset for Joint LLM and GNN Modeling
• Guided assembly of cellular network models from knowledge in literature
• Reverse enGENEering of regulatory networks from Big Data: a guide for a biologist
• Deciphering Cell Systems: Machine Learning Perspectives And Approaches For The Analysis Of Single-Cell Data