College of Computer Science and Technology, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
College of Software, and Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education, Jilin University, Changchun 130012, China.
Genes (Basel). 2022 Oct 21;13(10):1916. doi: 10.3390/genes13101916.
Melanoma is a lethal skin disease that develops from moles. This study aimed to integrate multimodal data to predict metastatic melanoma, which is highly aggressive and difficult to treat. The proposed EnsembleSKCM method evaluated the prediction performances of long noncoding RNAs (lncRNAs), protein-coding messenger genes (mRNAs) and pathology images (images) for metastatic melanoma. Feature selection was used to screen for metastatic biomarkers in the lncRNA and mRNA datasets. The integrated EnsembleSKCM model was built based on the weighted results of the lncRNA-, mRNA- and image-based models. EnsembleSKCM achieved 0.9444 in the prediction accuracy of metastatic melanoma and outperformed the single-modal prediction models based on the lncRNA, mRNA and image data. The experimental data suggest the importance of integrating the complementary information from the three data modalities. WGCNA was used to analyze the relationship of molecular-level features and image features, and the results show connections between them. Another cohort was used to validate our prediction.
黑色素瘤是一种致命的皮肤疾病，由痣发展而来。本研究旨在整合多模态数据来预测转移性黑色素瘤，这种疾病具有高度侵袭性且难以治疗。所提出的 EnsembleSKCM 方法评估了长链非编码 RNA（lncRNA）、蛋白质编码信使基因（mRNA）和病理图像（图像）对转移性黑色素瘤的预测性能。特征选择用于筛选 lncRNA 和 mRNA 数据集中的转移性生物标志物。基于 lncRNA、mRNA 和基于图像的模型的加权结果构建了集成的 EnsembleSKCM 模型。EnsembleSKCM 在转移性黑色素瘤的预测准确性方面达到了 0.9444，优于基于 lncRNA、mRNA 和图像数据的单模态预测模型。实验数据表明整合来自三种数据模态的互补信息的重要性。WGCNA 用于分析分子水平特征和图像特征之间的关系，结果显示它们之间存在联系。另一个队列用于验证我们的预测。
