Multi-label Learning for the Diagnosis of Cancer and Identification of Novel Biomarkers with High-throughput Omics
Multi-label Learning for the Diagnosis of Cancer and Identification of Novel Biomarkers with High-throughput Omics
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DOI：
10.2174/1574893615999200623130416
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发表时间：
2021-01-01
影响因子：
4
通讯作者：
Wang, Jinke
中科院分区：
文献类型：
--
作者：
Liu, Shicai;Tang, Hailin;Wang, Jinke
背景资料：生物信息学和机器学习技术的发展促进了癌症的诊断和基于组学的生物标志物的发现。目的：本研究采用一种新的数据驱动方法对正常胃肠道样本和不同类型的胃肠道肿瘤样本进行分类，寻找潜在的生物标志物，为胃肠道肿瘤患者的有效诊断和预后评估提供依据。采用不同的特征选择方法，并使用支持向量机（SVM）和随机森林（RF）models.Results的诊断性能进行基准测试的建议的生物签名。所有模型表现出令人满意的性能，其中多标记RF似乎是最好的。基于多标记RF的模型的准确率为83.12%，精确率、召回率、F1和Hamming损失分别为79.70%、68.31%、0.7357和0.1688。此外，提出的生物标志物特征与癌症的多方面特征高度相关。功能富集分析和候选生物标志物对患者预后的影响也进行了检查。结论：我们成功地引入了基于多标记学习的固体工作流程，用于癌症诊断和新生物标志物的鉴定。新的转录组生物签名，可以提高诊断准确性，在胃肠道癌症的介绍，在各种临床设置的进一步验证。
Background: The advancement of bioinformatics and machine learning has facilitated the diagnosis of cancer and the discovery of omics-based biomarkers.Objective: Our study employed a novel data-driven approach to classifying the normal samples and different types of gastrointestinal cancer samples, to find potential biomarkers for effective diagnosis and prognosis assessment of gastrointestinal cancer patients.Methods: Different feature selection methods were used, and the diagnostic performance of the proposed biosignatures was benchmarked using support vector machine (SVM) and random forest (RF) models.Results: All models showed satisfactory performance in which Multilabel-RF appeared to be the best. The accuracy of the Multilabel-RF based model was 83.12%, with precision, recall, F1, and Hamming- Loss of 79.70%, 68.31%, 0.7357 and 0.1688, respectively. Moreover, proposed biomarker signatures were highly associated with multifaceted hallmarks in cancer. Functional enrichment analysis and impact of the biomarker candidates in the prognosis of the patients were also examined.Conclusion: We successfully introduced a solid workflow based on multi-label learning with High- Throughput Omics for diagnosis of cancer and identification of novel biomarkers. Novel transcriptome biosignatures that may improve the diagnostic accuracy in gastrointestinal cancer are introduced for further validations in various clinical settings.