TreeQNet: a webserver for Treatment evaluation with Quantified Network

BACKGROUND: Personalized therapy has been at the forefront of cancer care, making cancer treatment more effective. Since cancer patients respond individually to drug therapy, predicting the sensitivity of each patient to specific drugs is very helpful to apply therapeutic agents. Traditional methods...

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Autores principales: Li, Zhenlei, Huang, Ya, Li, Qingrun, Sun, Yidi, Li, Chen, Wu, Jiarui, Zheng, Haoran, Zeng, Rong
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Software
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652860/
https://www.ncbi.nlm.nih.gov/pubmed/36368946
http://dx.doi.org/10.1186/s12859-022-05024-y
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author Li, Zhenlei
Huang, Ya
Li, Qingrun
Sun, Yidi
Li, Chen
Wu, Jiarui
Zheng, Haoran
Zeng, Rong
author_facet Li, Zhenlei
Huang, Ya
Li, Qingrun
Sun, Yidi
Li, Chen
Wu, Jiarui
Zheng, Haoran
Zeng, Rong
author_sort Li, Zhenlei
collection PubMed
description BACKGROUND: Personalized therapy has been at the forefront of cancer care, making cancer treatment more effective. Since cancer patients respond individually to drug therapy, predicting the sensitivity of each patient to specific drugs is very helpful to apply therapeutic agents. Traditional methods focus on node (molecular) information but ignore relevant interactions among different nodes, which has very limited application in complex situations, such as cancer drug responses in real clinical practice. RESULTS: Treatment evaluation with Quantified Network (TreeQNet) is a webserver which could predict sensitivity to drugs for patients through the innovative use of proteomic and phosphoproteomic network from tumor tissues. CONCLUSION: TreeQNet service: http://bioinfo.ustc.edu.cn/. TreeQNet source code: https://github.com/Really00/treeqnet-web-front/.
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spelling pubmed-96528602022-11-15 TreeQNet: a webserver for Treatment evaluation with Quantified Network Li, Zhenlei Huang, Ya Li, Qingrun Sun, Yidi Li, Chen Wu, Jiarui Zheng, Haoran Zeng, Rong BMC Bioinformatics Software BACKGROUND: Personalized therapy has been at the forefront of cancer care, making cancer treatment more effective. Since cancer patients respond individually to drug therapy, predicting the sensitivity of each patient to specific drugs is very helpful to apply therapeutic agents. Traditional methods focus on node (molecular) information but ignore relevant interactions among different nodes, which has very limited application in complex situations, such as cancer drug responses in real clinical practice. RESULTS: Treatment evaluation with Quantified Network (TreeQNet) is a webserver which could predict sensitivity to drugs for patients through the innovative use of proteomic and phosphoproteomic network from tumor tissues. CONCLUSION: TreeQNet service: http://bioinfo.ustc.edu.cn/. TreeQNet source code: https://github.com/Really00/treeqnet-web-front/. BioMed Central 2022-11-11 /pmc/articles/PMC9652860/ /pubmed/36368946 http://dx.doi.org/10.1186/s12859-022-05024-y Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Software
Li, Zhenlei
Huang, Ya
Li, Qingrun
Sun, Yidi
Li, Chen
Wu, Jiarui
Zheng, Haoran
Zeng, Rong
TreeQNet: a webserver for Treatment evaluation with Quantified Network
title TreeQNet: a webserver for Treatment evaluation with Quantified Network
title_full TreeQNet: a webserver for Treatment evaluation with Quantified Network
title_fullStr TreeQNet: a webserver for Treatment evaluation with Quantified Network
title_full_unstemmed TreeQNet: a webserver for Treatment evaluation with Quantified Network
title_short TreeQNet: a webserver for Treatment evaluation with Quantified Network
title_sort treeqnet: a webserver for treatment evaluation with quantified network
topic Software
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9652860/
https://www.ncbi.nlm.nih.gov/pubmed/36368946
http://dx.doi.org/10.1186/s12859-022-05024-y
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