DES-Amyloidoses “Amyloidoses through the looking-glass”: A knowledgebase developed for exploring and linking information related to human amyloid-related diseases

Vladan P. Bajic*, Adil Salhi, Katja Lakota, Aleksandar Radovanovic, Rozaimi Razali, Lada Zivkovic, Biljana Spremo-Potparevic, Mahmut Uludag, Faroug Tifratene, Olaa Motwalli, Benoit Marchand, Vladimir B. Bajic, Takashi Gojobori, Esma R. Isenovic, Magbubah Essack*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract

More than 30 types of amyloids are linked to close to 50 diseases in humans, the most prominent being Alzheimer’s disease (AD). AD is brain-related local amyloidosis, while another amyloidosis, such as AA amyloidosis, tends to be more systemic. Therefore, we need to know more about the biological entities’ influencing these amyloidosis processes. However, there is currently no support system developed specifically to handle this extraordinarily complex and demanding task. To acquire a systematic view of amyloidosis and how this may be relevant to the brain and other organs, we needed a means to explore "amyloid network systems" that may underly processes that leads to an amyloid-related disease. In this regard, we developed the DES-Amyloidoses knowledgebase (KB) to obtain fast and relevant information regarding the biological network related to amyloid proteins/peptides and amyloid-related diseases. This KB contains information obtained through text and data mining of available scientific literature and other public repositories. The information compiled into the DES-Amyloidoses system based on 19 topic-specific dictionaries resulted in 796,409 associations between terms from these dictionaries. Users can explore this information through various options, including enriched concepts, enriched pairs, and semantic similarity. We show the usefulness of the KB using an example focused on inflammasome-amyloid associations. To our knowledge, this is the only KB dedicated to human amyloid-related diseases derived primarily through literature text mining and complemented by data mining that provides a novel way of exploring information relevant to amyloidoses.

Original languageEnglish (US)
Article numbere0271737
JournalPloS one
Volume17
Issue number7 July
DOIs
StatePublished - Jul 2022

Bibliographical note

Funding Information:
This work has been supported by the Ministry of Education, Science and Technological Development, Republic of Serbia, and by the KAUST grant OSR#4129 (to EI and VBB), which also supported SZ and VPB. VBB has been supported by the KAUST Base Research Fund (BAS/1/1606-01-01), while ME has been supported by KAUST Office of Sponsored Research (OSR) grant no. FCC/1/1976-20-01. TG has been supported by the King Abdullah University of Science and Technology (KAUST) Base Research Fund (BAS/1/1059-01-01). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. This work is part of the collaboration between the Laboratory of Radiobiology and Molecular Genetics, Vinca Institute of Nuclear Sciences, University of Belgrade, Belgrade, Serbia and King Abdullah University of Science and Technology (KAUST), Computational Bioscience Research Center (CBRC), Thuwal, Saudi Arabia.

Publisher Copyright:
© 2022 Bajic et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

ASJC Scopus subject areas

  • General

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