ArticleOncotarget2022
CancerOmicsNet: a multi-omics network-based approach to anti-cancer drug profiling.
Article in Oncotarget, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
15 citing papers in PubMed.
- Network-based multi-omics integrative analysis methods in drug discovery: a systematic review.BioData mining · 2025Review
- Unlocking artificial intelligence, machine learning and deep learning to combat therapeutic resistance in metastatic castration-resistant prostate cancer: a comprehensive review.Ecancermedicalscience · 2025Review
- Model ensembling as a tool to form interpretable multi-omic predictors of cancer pharmacosensitivity.Briefings in bioinformatics · 2024Article
- Cancer pharmacoinformatics: Databases and analytical tools.Functional & integrative genomics · 2024Review
- SynerGNet: A Graph Neural Network Model to Predict Anticancer Drug Synergy.Biomolecules · 2024Article
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Scientific reports · 2024Article
- Review
- MMCL-CDR: enhancing cancer drug response prediction with multi-omics and morphology images contrastive representation learning.Bioinformatics (Oxford, England) · 2023Article
- Opportunities and challenges of 5G network technology toward precision medicine.Clinical and translational science · 2023Review
- Augmented drug combination dataset to improve the performance of machine learning models predicting synergistic anticancer effects.Research square · 2023Article
- Article
- Cancer Informatics 2023: Data Sharing and Federating Learning Point Towards New Collaborative Opportunities.Yearbook of medical informatics · 2023Article
- Deep learning methods for drug response prediction in cancer: Predominant and emerging trends.Frontiers in medicine · 2023Review
- Artificial intelligence to guide precision anticancer therapy with multitargeted kinase inhibitors.BMC cancer · 2022Article
- Omics Data and Data Representations for Deep Learning-Based Predictive Modeling.International journal of molecular sciences · 2022Review
Corrections and comments
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Authors and funding
4 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.