2021
DOI: 10.1093/bioinformatics/btab011
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SubtypeDrug: a software package for prioritization of candidate cancer subtype-specific drugs

Abstract: Summary Cancer can be classified into various subtypes by its molecular, histological or clinical characteristics. Discovering cancer-subtype-specific drugs is a crucial step in personalized medicine. SubtypeDrug is a system biology R-based software package that enables the prioritization of subtype-specific drugs based on cancer expression data from samples of many subtypes. This provides a novel approach to identify the subtype-specific drug by considering biological functions regulated by … Show more

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Cited by 29 publications
(17 citation statements)
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“…Deep learning ( Dao et al, 2021 ; Lv et al, 2021a , b ) also made a great contribution to the clinic, including skin cancer ( Esteva et al, 2017 ), breast cancer ( Liu J. et al, 2021 ), and brain diseases ( Liu G. et al, 2018 ; Liu et al, 2019 ; Bi et al, 2020 ; Hu et al, 2020 , 2021a , b ). In biological field, machine learning has been widely used to solve biological problems, including O -GlcNAcylation site prediction ( Jia et al, 2018 ), microbiology analysis ( Qu et al, 2019 ), microRNAs and cancer association prediction ( Zeng et al, 2018 ), lncRNAs ( Cheng et al, 2016 ; Deng et al, 2021 ), CircRNAs ( Fang et al, 2019 ; Zhao et al, 2019 ), DNA methylation site ( Wei et al, 2018b ; Zou et al, 2019 ; Dai et al, 2020 ), osteoporosis diagnoses ( Su et al, 2020b ), function prediction of proteins ( Wei et al, 2018a ; Wang H. et al, 2019 ; Deng et al, 2020b ; Ding et al, 2020a ; Su et al, 2020a ), nucleotide binding sites ( Ding et al, 2021b ), drug complex network analysis ( Ding et al, 2019 , 2020b , a ; Deng et al, 2020a ; Han et al, 2021 ; Liu H. et al, 2021 ), protein remote homology ( Liu B. et al, 2018 ), electron transport proteins ( Ru et al, 2019 ), and cell-specific replication.…”
Section: Introductionmentioning
confidence: 99%
“…Deep learning ( Dao et al, 2021 ; Lv et al, 2021a , b ) also made a great contribution to the clinic, including skin cancer ( Esteva et al, 2017 ), breast cancer ( Liu J. et al, 2021 ), and brain diseases ( Liu G. et al, 2018 ; Liu et al, 2019 ; Bi et al, 2020 ; Hu et al, 2020 , 2021a , b ). In biological field, machine learning has been widely used to solve biological problems, including O -GlcNAcylation site prediction ( Jia et al, 2018 ), microbiology analysis ( Qu et al, 2019 ), microRNAs and cancer association prediction ( Zeng et al, 2018 ), lncRNAs ( Cheng et al, 2016 ; Deng et al, 2021 ), CircRNAs ( Fang et al, 2019 ; Zhao et al, 2019 ), DNA methylation site ( Wei et al, 2018b ; Zou et al, 2019 ; Dai et al, 2020 ), osteoporosis diagnoses ( Su et al, 2020b ), function prediction of proteins ( Wei et al, 2018a ; Wang H. et al, 2019 ; Deng et al, 2020b ; Ding et al, 2020a ; Su et al, 2020a ), nucleotide binding sites ( Ding et al, 2021b ), drug complex network analysis ( Ding et al, 2019 , 2020b , a ; Deng et al, 2020a ; Han et al, 2021 ; Liu H. et al, 2021 ), protein remote homology ( Liu B. et al, 2018 ), electron transport proteins ( Ru et al, 2019 ), and cell-specific replication.…”
Section: Introductionmentioning
confidence: 99%
“…Although radiotherapy for cervical cancer has been widely used, the emergence of radioresistance has deeply affected its treatment effect ( Kaidar-Person et al, 2012 ; Han et al, 2021 ). Consequently, overcoming the radiotherapy resistance of cervical cancer and finding related accurate biomarkers are essential and urgently needed.…”
Section: Discussionmentioning
confidence: 99%
“…Recently, a number of calculated methods have been developed to identify dysregulated genes and pathways in cancer using molecular omics data (genome or transcriptome) ( Cheng et al, 2019 ; Han et al, 2020 ; Sheng et al, 2021 ). Moreover, some methods were proposed to prioritize cancer candidate drugs and prognostic markers ( Han et al, 2018 , 2021 ; Di et al, 2019 ). However, the methods for identifying immune cells driven by specific somatic mutations are mainly based on biological experiments, and as far as we have known, no calculated tools could systematically identify mutation-specific immune cells.…”
Section: Discussionmentioning
confidence: 99%