The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions

In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selecte...

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Detalles Bibliográficos
Autores principales: Ning, Xia, Zhang, Chi, Wang, Kai, Zhao, Zhongming, Mathé, Ewy
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Research
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079340/
https://www.ncbi.nlm.nih.gov/pubmed/32183782
http://dx.doi.org/10.1186/s12911-020-1051-1
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author Ning, Xia
Zhang, Chi
Wang, Kai
Zhao, Zhongming
Mathé, Ewy
author_facet Ning, Xia
Zhang, Chi
Wang, Kai
Zhao, Zhongming
Mathé, Ewy
author_sort Ning, Xia
collection PubMed
description In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selected after careful review of 105 articles that were submitted to the conference, and cover topics on deep learning for drug-target interaction prediction and data mining and visualization of high-order drug-drug interactions.
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spelling pubmed-70793402020-03-23 The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions Ning, Xia Zhang, Chi Wang, Kai Zhao, Zhongming Mathé, Ewy BMC Med Inform Decis Mak Research In this editorial, we briefly summarize the International Conference on Intelligent Biology and Medicine 2019 (ICIBM 2019) that was held on June 9–11, 2019 at Columbus, Ohio, USA. Then, we introduce the two research articles included in this supplement issue. These two research articles were selected after careful review of 105 articles that were submitted to the conference, and cover topics on deep learning for drug-target interaction prediction and data mining and visualization of high-order drug-drug interactions. BioMed Central 2020-03-18 /pmc/articles/PMC7079340/ /pubmed/32183782 http://dx.doi.org/10.1186/s12911-020-1051-1 Text en © The Author(s). 2020 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research
Ning, Xia
Zhang, Chi
Wang, Kai
Zhao, Zhongming
Mathé, Ewy
The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title_full The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title_fullStr The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title_full_unstemmed The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title_short The International Conference on Intelligent Biology and Medicine 2019: computational methods for drug interactions
title_sort international conference on intelligent biology and medicine 2019: computational methods for drug interactions
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7079340/
https://www.ncbi.nlm.nih.gov/pubmed/32183782
http://dx.doi.org/10.1186/s12911-020-1051-1
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