System Modeling and Simulation Laboratory, Department of Computer Science, University of the Philippines Diliman, Quezon City 1101, Philippines.
Center for Informatics, University of San Agustin, Iloilo City 5000, Philippines.
Genes (Basel). 2022 Dec 7;13(12):2303. doi: 10.3390/genes13122303.
Melanoma is considered to be the most serious and aggressive type of skin cancer, and metastasis appears to be the most important factor in its prognosis. Herein, we developed a transfer learning-based biomarker discovery model that could aid in the diagnosis and prognosis of this disease. After applying it to the ensemble machine learning model, results revealed that the genes found were consistent with those found using other methodologies previously applied to the same TCGA (The Cancer Genome Atlas) data set. Further novel biomarkers were also found. Our ensemble model achieved an AUC of 0.9861, an accuracy of 91.05, and an F1 score of 90.60 using an independent validation data set. This study was able to identify potential genes for diagnostic classification (C7 and GRIK5) and diagnostic and prognostic biomarkers (S100A7, S100A7, KRT14, KRT17, KRT6B, KRTDAP, SERPINB4, TSHR, PVRL4, WFDC5, IL20RB) in melanoma. The results show the utility of a transfer learning approach for biomarker discovery in melanoma.
黑色素瘤被认为是最严重和侵袭性最强的皮肤癌，转移似乎是其预后的最重要因素。在此，我们开发了一种基于迁移学习的生物标志物发现模型，可用于辅助诊断和预测这种疾病。将其应用于集成机器学习模型后，结果表明发现的基因与先前应用于相同 TCGA（癌症基因组图谱）数据集的其他方法所发现的基因一致。还发现了一些新的生物标志物。我们的集成模型使用独立验证数据集实现了 0.9861 的 AUC、91.05%的准确率和 90.60%的 F1 分数。这项研究能够识别潜在的诊断分类（C7 和 GRIK5）和诊断及预后生物标志物（S100A7、S100A7、KRT14、KRT17、KRT6B、KRTDAP、SERPINB4、TSHR、PVRL4、WFDC5、IL20RB）的候选基因。结果表明，迁移学习方法在黑色素瘤生物标志物发现中具有实用性。
