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

• URL: http://arxiv.org/abs/2011.03123v2
• Date: Mon, 9 Nov 2020 07:51:35 GMT
• Title: PubSqueezer: A Text-Mining Web Tool to Transform Unstructured Documents into Structured Data
• Authors: Alberto Calderone
• Abstract summary: I present a web tool which uses a Text Mining strategy to transform unstructured biomedical articles into structured data. generated results give a quick overview on complex topics which can possibly suggest not explicitly reported information. I show how a literature based analysis conducted with PubSqueezer results allows to describe known facts about SARS-CoV-2.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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

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