The research aims to improve the prediction of drug sensitivity on cancer cell lines using gene expression data and molecular fingerprints of drugs. The proposed study uses a deep learning model, BioMarkerX, trained on the Cancer Cell Line Encyclopedia (CCLE) and Genomics of Drug Sensitivity in Cancer (GDSC) datasets utilizing Particle Swarm Optimization technique to select specific genes as features. The model achieves high prediction accuracy with a Root Mean Square Error (RMSE) of 0.40 ± 0.02 and R2 of 0.83 ± 0.03 on the CCLE dataset, and an RMSE of 0.36 ± 0.05 and R2 of 0.83 ± 0.03 on the GDSC dataset. The approach also used an explainable artificial intelligence model to discover biological markers linked to cancer development. This can provide insights into targeted therapies for improving cancer treatment outcomes. Overall, the study presents an effective approach for identifying important biological markers relevant to cancer disease, aiding in the development of more efficient anticancer medications.

中文翻译：

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使用可解释的人工智能识别癌症疾病的生物标志物

该研究旨在利用基因表达数据和药物分子指纹来改进对癌细胞系药物敏感性的预测。拟议的研究使用深度学习模型 BioMarkerX，该模型在癌细胞系百科全书 (CCLE) 和癌症药物敏感性基因组学 (GDSC) 数据集上进行训练，利用粒子群优化技术来选择特定基因作为特征。该模型在CCLE数据集上的均方根误差（RMSE）为0.40±0.02，R2为0.83±0.03，在GDSC数据集上的RMSE为0.36±0.05，R2为0.83±0.03，实现了较高的预测精度。该方法还使用可解释的人工智能模型来发现与癌症发展相关的生物标记。这可以为改善癌症治疗结果的靶向治疗提供见解。总体而言，该研究提出了一种识别与癌症疾病相关的重要生物标志物的有效方法，有助于开发更有效的抗癌药物。