San Diego Supercomputer Center, University of California, San Diego, CA, USA.
MAP program, University of California, San Diego, CA, USA.
Oral Dis. 2021 Apr;27(3):484-493. doi: 10.1111/odi.13591. Epub 2020 Sep 7.
The aim of this research is the study of metabolic pathways related to oral cancer and periodontitis along with development of machine-learning model for elucidation of these diseases based on saliva metabolites of patients.
Data mining, metabolomic pathways analysis, study of metabolite-gene networks related to these diseases. Machine-learning and deep-learning methods for development of the model for recognition of oral cancer versus periodontitis, using patients' saliva.
The most accurate classifications between oral cancer and periodontitis were performed using neural networks, logistic regression and stochastic gradient descent confirmed by the separate 10-fold cross-validations. The best results were achieved by the deep-learning neural network with the TensorFlow program. Accuracy of the resulting model was 79.54%. The other methods, which did not rely on deep learning, were able to achieve comparable, although slightly worse results with respect to accuracy.
Our results demonstrate a possibility to distinguish oral cancer from periodontal disease by analysis the saliva metabolites of a patient, using machine-learning methods. These findings may be useful in the development of a non-invasive method to aid care providers in determining between oral cancer and periodontitis quickly and effectively.
本研究旨在研究与口腔癌和牙周炎相关的代谢途径，并基于患者唾液代谢物开发用于阐明这些疾病的机器学习模型。
数据挖掘、代谢组学途径分析、与这些疾病相关的代谢物-基因网络研究。使用患者唾液，开发用于区分口腔癌与牙周炎的机器学习和深度学习模型。
通过独立的 10 折交叉验证，使用神经网络、逻辑回归和随机梯度下降法对口腔癌和牙周炎进行了最准确的分类。使用 TensorFlow 程序的深度学习神经网络取得了最佳效果，准确率为 79.54%。其他不依赖于深度学习的方法在准确性方面也能取得相当但略差的结果。
我们的研究结果表明，通过分析患者的唾液代谢物，使用机器学习方法，可以区分口腔癌和牙周病。这些发现可能有助于开发一种非侵入性方法，以帮助医疗保健提供者快速有效地确定口腔癌和牙周炎之间的区别。
