A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications
Abstract Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the...
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creator | Shen, Bihan Feng, Fangyoumin Li, Kunshi Lin, Ping Ma, Liangxiao Li, Hong |
description | Abstract
Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios. |
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Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.</description><identifier>ISSN: 1467-5463</identifier><identifier>EISSN: 1477-4054</identifier><identifier>DOI: 10.1093/bib/bbac605</identifier><identifier>PMID: 36575826</identifier><language>eng</language><publisher>England: Oxford University Press</publisher><subject>Cancer therapies ; Cell culture ; Cell Line ; Deep Learning ; Drug development ; Humans ; In vitro methods and tests ; Performance evaluation ; Performance measurement ; Performance prediction ; Predictions</subject><ispartof>Briefings in bioinformatics, 2023-01, Vol.24 (1)</ispartof><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com 2022</rights><rights>The Author(s) 2022. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.</rights><rights>The Author(s) 2022. 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-c348t-8665a9691433674ac2c80a3331c2c49c1766d1049f1e82d1c2cb1850fe6430733</citedby><cites>FETCH-LOGICAL-c348t-8665a9691433674ac2c80a3331c2c49c1766d1049f1e82d1c2cb1850fe6430733</cites><orcidid>0000-0002-9103-4913 ; 0000-0003-4592-2554</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,1598,27903,27904</link.rule.ids><linktorsrc>$$Uhttps://dx.doi.org/10.1093/bib/bbac605$$EView_record_in_Oxford_University_Press$$FView_record_in_$$GOxford_University_Press</linktorsrc><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/36575826$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Shen, Bihan</creatorcontrib><creatorcontrib>Feng, Fangyoumin</creatorcontrib><creatorcontrib>Li, Kunshi</creatorcontrib><creatorcontrib>Lin, Ping</creatorcontrib><creatorcontrib>Ma, Liangxiao</creatorcontrib><creatorcontrib>Li, Hong</creatorcontrib><title>A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications</title><title>Briefings in bioinformatics</title><addtitle>Brief Bioinform</addtitle><description>Abstract
Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.</description><subject>Cancer therapies</subject><subject>Cell culture</subject><subject>Cell Line</subject><subject>Deep Learning</subject><subject>Drug development</subject><subject>Humans</subject><subject>In vitro methods and tests</subject><subject>Performance evaluation</subject><subject>Performance measurement</subject><subject>Performance prediction</subject><subject>Predictions</subject><issn>1467-5463</issn><issn>1477-4054</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp90d9L3TAUB_AwJtOpT3uXgDCEUU2aX61vF3FTuOCLPpc0PXWRNulyUsH_fi33ugcf9pRD-PDlcL6EfOPskrNaXLW-vWpb6zRTn8gRl8YUkin5eZ21KZTU4pB8RXxhrGSm4l_IodDKqKrUR2TeUHzDDKPN3lGLCIgjhExjTzuAiQ5gU_DhmY6Qf8cOaR8T7dL8TBPgFAMCnRJ03mUfwzXtUxypD_TV5xRpjtQNPnhnB2qnaViGleEJOejtgHC6f4_J08_bx5u7Yvvw6_5msy2ckFUuKq2VrXXNpRDaSOtKVzErhODLJGvHjdYdZ7LuOVRlt_62vFKsBy0FM0Ick4td7pTinxkwN6NHB8NgA8QZm9KoermKkSs9_0Bf4pzCsl0jOBdSc8PUon7slEsRMUHfTMmPNr01nDVrG83SRrNvY9Fn-8y5HaH7Z9_Pv4DvOxDn6b9JfwGd-ZMU</recordid><startdate>20230119</startdate><enddate>20230119</enddate><creator>Shen, Bihan</creator><creator>Feng, Fangyoumin</creator><creator>Li, Kunshi</creator><creator>Lin, Ping</creator><creator>Ma, Liangxiao</creator><creator>Li, Hong</creator><general>Oxford University Press</general><general>Oxford Publishing Limited (England)</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><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><orcidid>https://orcid.org/0000-0002-9103-4913</orcidid><orcidid>https://orcid.org/0000-0003-4592-2554</orcidid></search><sort><creationdate>20230119</creationdate><title>A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications</title><author>Shen, Bihan ; Feng, Fangyoumin ; Li, Kunshi ; Lin, Ping ; Ma, Liangxiao ; Li, Hong</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c348t-8665a9691433674ac2c80a3331c2c49c1766d1049f1e82d1c2cb1850fe6430733</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Cancer therapies</topic><topic>Cell culture</topic><topic>Cell Line</topic><topic>Deep Learning</topic><topic>Drug development</topic><topic>Humans</topic><topic>In vitro methods and tests</topic><topic>Performance evaluation</topic><topic>Performance measurement</topic><topic>Performance prediction</topic><topic>Predictions</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Shen, Bihan</creatorcontrib><creatorcontrib>Feng, Fangyoumin</creatorcontrib><creatorcontrib>Li, Kunshi</creatorcontrib><creatorcontrib>Lin, Ping</creatorcontrib><creatorcontrib>Ma, Liangxiao</creatorcontrib><creatorcontrib>Li, Hong</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><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>Shen, Bihan</au><au>Feng, Fangyoumin</au><au>Li, Kunshi</au><au>Lin, Ping</au><au>Ma, Liangxiao</au><au>Li, Hong</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications</atitle><jtitle>Briefings in bioinformatics</jtitle><addtitle>Brief Bioinform</addtitle><date>2023-01-19</date><risdate>2023</risdate><volume>24</volume><issue>1</issue><issn>1467-5463</issn><eissn>1477-4054</eissn><abstract>Abstract
Drug response prediction is an important problem in personalized cancer therapy. Among various newly developed models, significant improvement in prediction performance has been reported using deep learning methods. However, systematic comparisons of deep learning methods, especially of the transferability from preclinical models to clinical cohorts, are currently lacking. To provide a more rigorous assessment, the performance of six representative deep learning methods for drug response prediction using nine evaluation metrics, including the overall prediction accuracy, predictability of each drug, potential associated factors and transferability to clinical cohorts, in multiple application scenarios was benchmarked. Most methods show promising prediction within cell line datasets, and TGSA, with its lower time cost and better performance, is recommended. Although the performance metrics decrease when applying models trained on cell lines to patients, a certain amount of power to distinguish clinical response on some drugs can be maintained using CRDNN and TGSA. With these assessments, we provide a guidance for researchers to choose appropriate methods, as well as insights into future directions for the development of more effective methods in clinical scenarios.</abstract><cop>England</cop><pub>Oxford University Press</pub><pmid>36575826</pmid><doi>10.1093/bib/bbac605</doi><orcidid>https://orcid.org/0000-0002-9103-4913</orcidid><orcidid>https://orcid.org/0000-0003-4592-2554</orcidid></addata></record> |
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subjects | Cancer therapies Cell culture Cell Line Deep Learning Drug development Humans In vitro methods and tests Performance evaluation Performance measurement Performance prediction Predictions |
title | A systematic assessment of deep learning methods for drug response prediction: from in vitro to clinical applications |
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