ArticleCancers2023
Unlocking the Potential of Kinase Targets in Cancer: Insights from CancerOmicsNet, an AI-Driven Approach to Drug Response Prediction in Cancer.
Article in Cancers, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 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
17 citing papers in PubMed, 29 citations in OpenAlex.
- An integrative machine learning and structure-driven drug repositioning strategy for human IRAK4-targeted cancer therapy.Naunyn-Schmiedeberg's archives of pharmacology · 2026Article
- Deep Generative AI for Multi-Target Therapeutic Design: Toward Self-Improving Drug Discovery Framework.International journal of molecular sciences · 2025Review
- Aurora Kinase A inhibitor alisertib failed to exert its efficacy on TNBC cells due to consequential enrichment of polyploid giant cancer cells (PGCCs).Discover oncology · 2025Article
- Exploring Artificial Intelligence's Potential to Enhance Conventional Anticancer Drug Development.Drug development research · 2025Review
- Kaempferols from Echinacea purpurea demonstrate anti-cancer potential by targeting anexelekto in breast cancer therapy using chemoinformatics approach.Discover oncology · 2025Article
- Leveraging artificial intelligence and machine learning in kinase inhibitor development: advances, challenges, and future prospects.RSC medicinal chemistry · 2025Review
- Machine Learning for Multi-Target Drug Discovery: Challenges and Opportunities in Systems Pharmacology.Pharmaceutics · 2025Review
- Integrating Graph Convolution and Attention Mechanism for Kinase Inhibition Prediction.Molecules (Basel, Switzerland) · 2025Article
- Intelligent deep learning model for targeted cancer drug delivery.Scientific reports · 2025Article
- Genomic Landscape of Breast Cancer: Study Across Diverse Ethnic Groups.Diseases (Basel, Switzerland) · 2025Article
- Inhibition of Kinase Activity and In Vitro Downregulation of the Protein Kinases in Lung Cancer and Cervical Cancer Cell Lines and the Identified Known Anticancer Compounds ofPlants (Basel, Switzerland) · 2025Article
- Diabetes mellitus and glymphatic dysfunction: Roles for oxidative stress, mitochondria, circadian rhythm, artificial intelligence, and imaging.World journal of diabetes · 2025Review
- Evaluation of Novel Diaza Cage Compounds as MRP Modulators in Cancer Cells.Anti-cancer agents in medicinal chemistry · 2025Article
- Article
- Kinome-Wide Virtual Screening by Multi-Task Deep Learning.International journal of molecular sciences · 2024Article
- Systems Pharmacodynamic Model of Combined Gemcitabine and Trabectedin in Pancreatic Cancer Cells. Part I: Effects on Signal Transduction Pathways Related to Tumor GrowthJournal of pharmaceutical sciences · 2024Article
- Hesperidin increases theJournal of advanced pharmaceutical technology & researchArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
10 authors at 1 institution in 1 country.
Funding
Abstract
Deregulated protein kinases are crucial in promoting cancer cell proliferation and driving malignant cell signaling. Although these kinases are essential targets for cancer therapy due to their involvement in cell development and proliferation, only a small part of the human kinome has been targeted by drugs. A comprehensive scoring system is needed to evaluate and prioritize clinically relevant kinases. We recently developed CancerOmicsNet, an artificial intelligence model employing graph-based algorithms to predict the cancer cell response to treatment with kinase inhibitors. The performance of this approach has been evaluated in large-scale benchmarking calculations, followed by the experimental validation of selected predictions against several cancer types. To shed light on the decision-making process of CancerOmicsNet and to better understand the role of each kinase in the model, we employed a customized saliency map with adjustable channel weights. The saliency map, functioning as an explainable AI tool, allows for the analysis of input contributions to the output of a trained deep-learning model and facilitates the identification of essential kinases involved in tumor progression. The comprehensive survey of biomedical literature for essential kinases selected by CancerOmicsNet demonstrated that it could help pinpoint potential druggable targets for further investigation in diverse cancer types.
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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.