Large scale text mining for deriving useful insights: A case study focused on microbiome.

ABSTRACT: Text mining has been shown to be an auxiliary but key driver for modeling, data harmonization, and interpretation in bio-medicine. Scientific literature holds a wealth of information and embodies cumulative knowledge and remains the core basis on which mechanistic pathways, molecular databases, and models are built and refined. Text mining provides the necessary tools to automatically harness the potential of text. In this study, we show the potential of large-scale text mining for deriving novel insights, with a focus on the growing field of microbiome. We first collected the complete set of abstracts relevant to the microbiome from PubMed and used our text mining and intelligence platform Taxila for analysis. We drive the usefulness of text mining using two case studies. First, we analyz

SUBMITTER: Ahmed SAJA 

PROVIDER: S-EPMC9473635 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature