人工智能算法在早期肝癌相关诊断和预后生物标志物识别中的潜力
Potential of Artificial Intelligence Algorithms for Identification of Relevant Diagnostic and Prognostic Biomarkers of Early-Stage Liver Cancer
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中文总结 AI 辅助
本研究利用深度学习和可解释AI，基于转录组数据识别肝细胞癌生物标志物，最佳模型准确率达90.74%，并发现DNAJB14为关键基因，其抑制可逆转肿瘤恶性特征。
中文摘要 AI 辅助
本研究探讨了利用深度学习和可解释人工智能来诊断肝细胞癌（HCC），并利用通过半监督学习从三个源数据集构建的转录组生物标志物HCC数据集，在疾病发展的五个不同阶段定义有效的生物标志物。研究进行了多次深度学习实验，采用不同的特征提取技术和基因集，以确定训练高精度、低损失模型的最有效特征。使用SelectKBest算法选择15个基因的最佳模型达到了90.74%的准确率，而使用20个选定基因的模型获得了最低记录损失0.3187。为解决数据集中的类别不平衡问题，采用了加权训练方法，并且为了模型的透明性和可解释性，基于SHAP的XAI分析提供了对模型决策过程的见解，一致发现DNAJB14是最具影响力的基因。本研究的功能验证提供了令人信服的证据，表明DNAJB14在HCC的不良特性中发挥重要作用，其抑制有效逆转了肿瘤细胞迁移、侵袭、集落和球体形成。本研究的主要局限性在于数据集的类别不平衡，虽然加权训练有助于缓解这一问题，但仍需进一步研究和更多数据来保证模型的泛化能力。未来的研究还应探索遗传变异、环境因素和临床差异对不同人群模型性能的影响。
英文摘要
This study explores the use of deep learning and explainable artificial intelligence to diagnose hepatocellular carcinoma (HCC) and define effective biomarkers across five different stages of disease development using a transcriptomic biomarker HCC dataset constructed via semi-supervised learning from three source datasets. Several deep learning experiments were conducted with different feature extraction techniques and gene sets to identify the most effective features for training high-accuracy models with minimal loss. The best-performing model, using 15 selected genes with the SelectKBest algorithm, achieved 90.74% accuracy, while the model with the lowest recorded loss of 0.3187 was obtained using 20 selected genes. To address the issue of class imbalance in the dataset, a weighted training approach was conducted, and for model transparency and interpretability a SHAP-based XAI analysis provided insights into the model's decision-making, consistently finding DNAJB14 as the most influential gene. Functional validation in this study has provided compelling evidence that DNAJB14 plays an important role in the adverse properties of HCC and that its inhibition effectively reverses tumour cell migration, invasion, colony and sphere formation. The main limitation of this study is the dataset's class imbalance, and while weighted training helped mitigate this, further research and additional data are needed to guarantee model generalizability. Future studies should also explore the influence of genetic variations, environmental factors, and clinical differences on model performance across diverse populations.
发表机构
- University of Sharjah（沙迦大学）
- University Medical Center Schleswig-Holstein（石勒苏益格-荷尔斯泰因大学医学中心）
- University of Lubeck（吕贝克大学）
- University of Birmingham（伯明翰大学）
机构由 AI 辅助整理，请以论文原文为准。
