Text-mining and Literature-based Discovery publishes on a range of approaches that take a body of research literature as the input, apply a series of computational, manual, or hybrid processes, and finally generate hypotheses that are potentially novel and meaningful for further investigations.
In the era of 4th industrial revolution, the availability of an enormous amount of unstructured data from various different sources creates both enormous opportunities and significant challenges. In biomedicine, for instance, the results of experiments or clinical trials are often only discovered long after they are first published, and this long-elapsed time may lead to failure of timely translation of research results into clinical practice or missed hypotheses for effective new drugs or treatments. Literature-based discovery (LBD) enabled by text mining aims to address this problem by leveraging what is already known from research, as published in the literature, to make connections and generate new research questions through inference. Although LBD originated in the biomedical domain, we have observed that it is being actively explored in other domains including psychology, economics, and climate science.
Text-mining and Literature-based Discovery invites papers addressing emerging challenges in a range of approaches that take a body of research literature as the input, apply a series of computational, manual, or hybrid processes, and finally generate hypotheses that are potentially novel and meaningful for further investigations.
The section is particularly interested in papers on:
• Natural language processing
• Entity extraction and relation extraction
• Intelligent or semantic search
• Full-text mining
• Entity linking
• Text-based hypothesis generation
• Mining clinical texts
• Evaluation and validation, and trust and privacy as it is relevant to text mining
High quality papers that go beyond these topics or integrate them in novel ways are equally welcome.
Indexed in: Google Scholar, DOAJ, CrossRef, CLOCKSS
PMCID: coming soon for all published articles
Text-mining and Literature-based Discovery welcomes submissions of the following article types: Brief Research Report, Conceptual Analysis, Correction, Data Report, Editorial, General Commentary, Hypothesis and Theory, Methods, Mini Review, Opinion, Original Research, Perspective, Review, Specialty Grand Challenge, Systematic Review and Technology and Code.
All manuscripts must be submitted directly to the section Text-mining and Literature-based Discovery, where they are peer-reviewed by the Associate and Review Editors of the specialty section.
Articles published in the section Text-mining and Literature-based Discovery will benefit from the Frontiers impact and tiering system after online publication. Authors of published original research with the highest impact, as judged democratically by the readers, will be invited by the Chief Editor to write a Frontiers Focused Review - a tier-climbing article. This is referred to as "democratic tiering". The author selection is based on article impact analytics of original research published in all Frontiers specialty journals and sections. Focused Reviews are centered on the original discovery, place it into a broader context, and aim to address the wider community across all of Research Metrics and Analytics.
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