Fwd: [DBWorld] Text-mining and Literature-based Discovery, on Information Extraction from Bio-Chemical Text.

Camilo Thorne

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68165, Mannheim, Germany
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"Exegi monumentum aere perennius"
(Horatius, Ode III-30)


---------- Forwarded message ---------
From: Ahmed Abdeen Hamed PhD via DBWorld <dbworld@cs.wisc.edu>
Date: Mon, Nov 9, 2020 at 8:52 PM
Subject: [DBWorld] Text-mining and Literature-based Discovery, on
Information Extraction from Bio-Chemical Text.
To: <dbworld@cs.wisc.edu>


We invite submissions for a Special issue of Frontiers in Research Metrics
and Analytics, Text-mining and Literature-based Discovery, on
Information Extraction from Bio-Chemical Text.

https://www.frontiersin.org/research-topics/16035/information-extraction-from-bio-chemical-text

Key Dates
Abstract registration: 01 December 2020
Manuscript submission deadline: 20 February 2021

Background
The volume of chemical and biochemical research, made available via
scientific publications and patents, is rapidly increasing. With such
explosive growth, it is extremely challenging for scientists to keep up to
date with all of the new discoveries and advancements even within
relatively focused discipline areas. Thus, there has been a surge of
interest in automated text mining tools to aid scientists in coping with
the explosive growth of research texts, to allow efficient and effective
knowledge extraction from these data. In chemistry and biochemistry, key
information related to synthesis, properties, and mode of action of
chemicals is critical for pharmaceutical and life sciences applications yet
is often only described in natural language texts.

Goal
Biochemical texts contain a wealth of information, and in this research
topic we aim to explore the application of text mining methods that
facilitate analysis and transformation of unstructured natural language
descriptions of chemicals and their interrelationships into actionable,
structured knowledge. The complexity of biochemical texts creates several
challenges to achieving these goals. First, the lexicon of biochemical
texts usually consists of extensive domain-specific terminology, rendering
the use of resources developed for general language processing ineffective.
Second, the scientific literature is usually written in a formal way
resulting in complex sentence structures. Finally, biochemical texts often
couple chemical structure information with linguistic descriptions,
resulting in texts that contain images, figures, and tables conveying
critical information. To this end, this special journal issue calls for
novel approaches to address these challenges, aiming at!
  improvin
 g the effectiveness of text mining in biochemical data.

Scope
This Research Topic calls for research papers addressing natural language
processing or text mining of chemical or biochemical texts, including
scientific literature or patents. The nominated research themes include but
are not limited to:

-       information extraction tasks such as chemical or drug named entity
recognition and identification of relations between chemical entities
-       biochemical document summarization or classification
-       construction of knowledge bases or knowledge graphs from texts,
e.g. for pharmacogenomics or chemical synthesis.

Methods that target the identification of crucial information in relevant
texts, such as chemical entities and their properties, details of chemical
reactions or synthesis, or interactions between chemicals, biological
molecules or genetic variation are welcome. We also welcome chemical-based
algorithms, tools, and methods targeting the identification of drug-drug
interactions or drug repurposing evidence in biomedical text.

Research that addresses the particular linguistic characteristics of
biochemical texts, including resource development such as annotated corpora
or domain-specific terminologies, or methods for constituent components of
a chemical text mining system, including specialized domain-specific
tokenization or chemical structure analysis, are also in scope.

Submission details:
https://www.frontiersin.org/research-topics/16035/information-extraction-from-bio-chemical-text

Please note that Abstract submission is non-binding, and serves as an
Expression of Interest.


Topic Editors:
Karin Verspoor, The University of Melbourne (Australia)
Jiayuan (Estrid) He, The University of Melbourne (Australia)
Ahmed Abdeen Hamed, Norwich University (Vermont, USA)
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