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CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling
Oncotarget, 2022, Vol.13 (1), p.695-706
Copyright: © 2022 Pu et al. ;ISSN: 1949-2553 ;EISSN: 1949-2553 ;DOI: 10.18632/oncotarget.28234 ;PMID: 35601606
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Title:
CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling
Author:
Pu, Limeng
;
Singha, Manali
;
Ramanujam, Jagannathan
;
Brylinski, Michal
Subjects:
Research Paper
Is Part Of:
Oncotarget, 2022, Vol.13 (1), p.695-706
Description:
Development of novel anti-cancer treatments requires not only a comprehensive knowledge of cancer processes and drug mechanisms of action, but also the ability to accurately predict the response of various cancer cell lines to therapeutics. Numerous computational methods have been developed to address this issue, including algorithms employing supervised machine learning. Nonetheless, high prediction accuracies reported for many of these techniques may result from a significant overlap among training, validation, and testing sets, making existing predictors inapplicable to new data. To address these issues, we developed CancerOmicsNet, a graph neural network with sophisticated attention propagation mechanisms to predict the therapeutic effects of kinase inhibitors across various tumors. Emphasizing on the system-level complexity of cancer, CancerOmicsNet integrates multiple heterogeneous data, such as biological networks, genomics, inhibitor profiling, and gene-disease associations, into a unified graph structure. The performance of CancerOmicsNet, properly cross-validated at the tissue level, is 0.83 in terms of the area under the receiver operating characteristics, which is notably higher than those measured for other approaches. CancerOmicsNet generalizes well to unseen data, i.e., it can predict therapeutic effects across a variety of cancer cell lines and inhibitors. CancerOmicsNet is freely available to the academic community at https://github.com/pulimeng/CancerOmicsNet.
Publisher:
United States: Impact Journals LLC
Language:
English
Identifier:
ISSN: 1949-2553
EISSN: 1949-2553
DOI: 10.18632/oncotarget.28234
PMID: 35601606
Source:
Geneva Foundation Free Medical Journals at publisher websites
PubMed Central
Alma/SFX Local Collection
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