Trending With Impact: CancerOmicsNet for Anti-Cancer Drug Profiling

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Oncotarget

Oncotarget published this trending research paper on May 19, 2022 in Volume 13, entitled, "CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling" by researchers from the Center for Computation and Technology, Louisiana State University, Baton Rouge, LA; Department of Biological Sciences, Louisiana State University, Baton Rouge, LA; Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, LA. DOI - https://doi.org/10.18632/oncotarget.28234 Correspondence to - Michal Brylinski - michal@brylinski.org Abstract 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. Sign up for free Altmetric alerts about this article - https://oncotarget.altmetric.com/details/email_updates?id=10.18632%2Foncotarget.28234 Press release - https://www.oncotarget.com/news/pr/oncotarget-anti-cancer-drug-profiling-with-canceromicsnet/ Keywords - cancer growth rate, kinase inhibitors, differential gene expression, gene-disease association, cancer-specific networks About Oncotarget Oncotarget is a peer-reviewed, open access biomedical journal covering research on all aspects of oncology. To learn more about Oncotarget, please visit https://www.oncotarget.com and connect with us: SoundCloud - https://soundcloud.com/oncotarget Facebook - https://www.facebook.com/Oncotarget/ Twitter - https://twitter.com/oncotarget Instagram - https://www.instagram.com/oncotargetjrnl/ YouTube - https://www.youtube.com/OncotargetYouTube LinkedIn - https://www.linkedin.com/company/oncotarget Pinterest - https://www.pinterest.com/oncotarget/ Reddit - https://www.reddit.com/user/Oncotarget/ Oncotarget is published by Impact Journals, LLC: https://www.ImpactJournals.com Media Contact MEDIA@IMPACTJOURNALS.COM 18009220957