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ThinkMind // International Journal On Advances in Life Sciences, volume 8, numbers 3 and 4, 2016 // View article lifsci_v8_n34_2016_13


Improved Knowledge Acquisition and Creation of Structured Knowledge for Systems Toxicology

Authors:
Sam Ansari
Justyna Szostak
Marja Talikka
Juliane Fluck
Julia Hoeng

Keywords: text mining; BEL; knowledge management; network models; curation; systems toxicology

Abstract:
Ever-increasing scientific literature enhances our understanding on how toxicants impact biological systems. In order to utilize this information in the growing field of systems toxicology, the published data must be transformed into a structured format suitable for knowledge modelling, reasoning, and ultimately high throughput data analysis and interpretation. Consequently, there is an increasing demand from systems toxicologists to access such knowledge in a computable format, here biological network models. The BEL Information Extraction workFlow (BELIEF) automatically extracts biological entities and causal relationships from any text resource and converts them into a formalized language, the Biological Expression Language (BEL). BEL is a machine- and human-readable language that represents molecular relationships and events as semantic triples: subject–relationship–object. In addition to the automatic extraction through text mining, BELIEF also features a curation interface to verify and modify the proposed triples and benefits from BEL’s human-readability. The curation interface facilitates this curation task by providing relevant information to ensure high curation accuracy and fast processing. The resulting BEL triples are then assembled to biological network models that represent specific biological processes for a given context, e.g., organism, tissue type, disease state. These biological network models can then be verified in a crowd-based approach utilizing a collaborative web-based platform before finally sharing them through a publicly available and specialized repository. In this strategy paper, we summarize over various solutions to challenges in the knowledge-based systems toxicological assessment.

Pages: 309 to 319

Copyright: Copyright (c) to authors, 2016. Used with permission.

Publication date: December 31, 2016

Published in: journal

ISSN: 1942-2660

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