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

TL;DR

This paper presents a method for converting machine reading outputs from biological literature into executable cellular signaling models, addressing challenges and demonstrating a case study in pancreatic cancer.

Contribution

It introduces a novel approach to translating diverse machine reading outputs into discrete cellular network models, facilitating automated biological modeling.

Findings

Successfully translated machine reading outputs into cellular models.

Identified key issues in assembling models from current reading engines.

Demonstrated approach with a pancreatic cancer case study.

Abstract

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