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Prediction of drug adverse events using deep learning in pharmaceutical discovery
Abstract Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drug...
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Published in: | Briefings in bioinformatics 2021-03, Vol.22 (2), p.1884-1901 |
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creator | Lee, Chun Yen Chen, Yi-Ping Phoebe |
description | Abstract
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing. |
doi_str_mv | 10.1093/bib/bbaa040 |
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Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.</description><identifier>ISSN: 1477-4054</identifier><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbaa040</identifier><identifier>PMID: 32349125</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Classification ; Deep learning ; Drug interaction ; Drug interactions ; Drugs ; Learning algorithms ; Machine learning ; Side effects</subject><ispartof>Briefings in bioinformatics, 2021-03, Vol.22 (2), p.1884-1901</ispartof><rights>The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2020</rights><rights>The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2020. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c414t-58dc0b7ae96b949ee51f73f62f7dc241658d5e3b6e259876d82ab05d891ba8213</citedby><cites>FETCH-LOGICAL-c414t-58dc0b7ae96b949ee51f73f62f7dc241658d5e3b6e259876d82ab05d891ba8213</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,1604,27924,27925</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbaa040$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/32349125$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lee, Chun Yen</creatorcontrib><creatorcontrib>Chen, Yi-Ping Phoebe</creatorcontrib><title>Prediction of drug adverse events using deep learning in pharmaceutical discovery</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.</description><subject>Classification</subject><subject>Deep learning</subject><subject>Drug interaction</subject><subject>Drug interactions</subject><subject>Drugs</subject><subject>Learning algorithms</subject><subject>Machine learning</subject><subject>Side effects</subject><issn>1477-4054</issn><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><recordid>eNp90EtLAzEUBeAgitXqyr0EBBGkNskkk8lSii8oqKDrIZncqVPmZTIp9N-b0iriwtXNhS-Hy0HojJIbSlQyNZWZGqM14WQPHVEu5YQTwfd_vUfo2PslIYzIjB6iUcISrigTR-j1xYGtiqHqWtyV2LqwwNquwHnAsIJ28Dj4ql1gC9DjGrRrN1vV4v5Du0YXEIaq0DW2lS-6-G99gg5KXXs43c0xer-_e5s9TubPD0-z2_mk4JQPE5HZghipQaVGcQUgaCmTMmWltAXjNI1AQGJSYEJlMrUZ04YImylqdMZoMkZX29zedZ8B_JA38QSoa91CF3zOEpVmgotMRnrxhy674Np4Xc4EU0rSVCZRXW9V4TrvHZR576pGu3VOSb5pOo9N57umoz7fZQbTgP2x39VGcLkFXej_TfoCXkeGuw</recordid><startdate>20210322</startdate><enddate>20210322</enddate><creator>Lee, Chun Yen</creator><creator>Chen, Yi-Ping Phoebe</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7QO</scope><scope>7SC</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>K9.</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope><scope>P64</scope><scope>RC3</scope><scope>7X8</scope></search><sort><creationdate>20210322</creationdate><title>Prediction of drug adverse events using deep learning in pharmaceutical discovery</title><author>Lee, Chun Yen ; Chen, Yi-Ping Phoebe</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c414t-58dc0b7ae96b949ee51f73f62f7dc241658d5e3b6e259876d82ab05d891ba8213</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Classification</topic><topic>Deep learning</topic><topic>Drug interaction</topic><topic>Drug interactions</topic><topic>Drugs</topic><topic>Learning algorithms</topic><topic>Machine learning</topic><topic>Side effects</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lee, Chun Yen</creatorcontrib><creatorcontrib>Chen, Yi-Ping Phoebe</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>Biotechnology Research Abstracts</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><collection>Biotechnology and BioEngineering Abstracts</collection><collection>Genetics Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Briefings in bioinformatics</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Lee, Chun Yen</au><au>Chen, Yi-Ping Phoebe</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction of drug adverse events using deep learning in pharmaceutical discovery</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2021-03-22</date><risdate>2021</risdate><volume>22</volume><issue>2</issue><spage>1884</spage><epage>1901</epage><pages>1884-1901</pages><issn>1477-4054</issn><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Abstract
Traditional machine learning methods used to detect the side effects of drugs pose significant challenges as feature engineering processes are labor-intensive, expert-dependent, time-consuming and cost-ineffective. Moreover, these methods only focus on detecting the association between drugs and their side effects or classifying drug–drug interaction. Motivated by technological advancements and the availability of big data, we provide a review on the detection and classification of side effects using deep learning approaches. It is shown that the effective integration of heterogeneous, multidimensional drug data sources, together with the innovative deployment of deep learning approaches, helps reduce or prevent the occurrence of adverse drug reactions (ADRs). Deep learning approaches can also be exploited to find replacements for drugs which have side effects or help to diversify the utilization of drugs through drug repurposing.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>32349125</pmid><doi>10.1093/bib/bbaa040</doi><tpages>18</tpages></addata></record> |
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subjects | Classification Deep learning Drug interaction Drug interactions Drugs Learning algorithms Machine learning Side effects |
title | Prediction of drug adverse events using deep learning in pharmaceutical discovery |
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