Paper

PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data

Calderone, Alberto

2020

Overview

The amount of scientific papers published every day is daunting and constantly increasing. Keeping up with literature represents a challenge. If one wants to start exploring new topics it is hard to have a big picture without reading lots of articles. Furthermore, as one reads through literature, making mental connections is crucial to ask new questions which might lead to discoveries. In this work, I present a web tool which uses a Text Mining strategy to transform large collections of unstructured biomedical articles into structured data. Generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. In particular, I show two Data Science analyses. First, I present a literature based rare diseases network build using this tool in the hope that it will help clarify some aspects of these less popular pathologies. Secondly, I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2. In one sentence, data generated with PubSqueezer make it easy to use scientific literate in any computational analysis such as machine learning, natural language processing etc. Availability: http://www.pubsqueezer.com

• Publisher: Cornell University Library, arXiv.org
• Subject: Biomedical data / Data mining / Machine learning / Natural language processing / Scientific papers / Severe acute respiratory syndrome coronavirus 2 / Structured data / Unstructured data / Webs