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.
失调的蛋白激酶对促进癌细胞增殖及驱动恶性细胞信号传导至关重要。尽管这些激酶因参与细胞发育与增殖过程而成为癌症治疗的关键靶点，但目前药物仅能靶向人类激酶组中的一小部分。我们需要一套全面的评分系统来评估并优先排序临床相关激酶。近期我们开发了CancerOmicsNet人工智能模型，该模型采用基于图论的算法来预测癌细胞对激酶抑制剂的治疗反应。此方法的性能已通过大规模基准计算评估，并针对多种癌症类型的选择性预测进行了实验验证。为阐明CancerOmicsNet的决策机制并深入理解各激酶在模型中的作用，我们采用了具有可调节通道权重的定制化显著图。该显著图作为可解释人工智能工具，能够分析输入因素对训练后深度学习模型输出的贡献，并有助于识别参与肿瘤进展的关键激酶。通过对CancerOmicsNet筛选关键激酶进行的生物医学文献系统调研表明，该模型可帮助确定潜在药物靶点，为多种癌症类型的深入研究提供方向。
肿瘤(癌症)患者之家