Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification
Machine Learning-Based Ensemble Recursive Feature Selection of Circulating miRNAs for Cancer Tumor Classification
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DOI：
10.3390/cancers12071785
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发表时间：
2020-07-01
期刊：
影响因子：
5.2
通讯作者：
Tonda, Alberto
中科院分区：
文献类型：
--
作者：
Lopez-Rincon, Alejandro;Mendoza-Maldonado, Lucero;Tonda, Alberto
循环microRNA（miRNA）是小的非编码RNA分子，可以在体液中检测到，而无需对患者进行重大侵入性操作。miRNA作为肿瘤的生物标志物，在评估其存在和预测其类型和亚型方面显示出巨大的前景。最近，由于miRNA数据集的可用性，机器学习技术已成功应用于肿瘤分类。然而，这些结果很难被医学专家评估和解释，因为这些算法利用了数千种miRNAs的信息。在这项工作中，我们提出了一种新的技术，旨在将必要的信息减少到最小的循环miRNA集。实现的降维反映了潜在的、临床上可操作的、循环的基于miRNA的精准医学管道中非常重要的第一步。虽然目前正在讨论是否可以采取这第一步，我们在这里证明，它是可以执行分类任务，通过利用递归特征消除程序，集成了高质量的，国家的最先进的分类器上循环的miRNA的异质性合奏。异构集成可以通过使用不同的分类算法来补偿分类器的固有偏差。然后，选择特征进一步消除了使用来自不同研究或批次的数据所产生的偏差，从而产生更稳健和可靠的结果。该方法首先在肿瘤分类问题上进行测试，以分离10种不同类型的癌症，并在10个不同的临床试验中收集样本，然后在癌症亚型分类任务上进行评估，目的是将三阴性乳腺癌与其他乳腺癌亚型区分开来。总体而言，所提出的方法被证明是有效的，并与其他国家的最先进的特征选择方法相比。
Circulating microRNAs (miRNA) are small noncoding RNA molecules that can be detected in bodily fluids without the need for major invasive procedures on patients. miRNAs have shown great promise as biomarkers for tumors to both assess their presence and to predict their type and subtype. Recently, thanks to the availability of miRNAs datasets, machine learning techniques have been successfully applied to tumor classification. The results, however, are difficult to assess and interpret by medical experts because the algorithms exploit information from thousands of miRNAs. In this work, we propose a novel technique that aims at reducing the necessary information to the smallest possible set of circulating miRNAs. The dimensionality reduction achieved reflects a very important first step in a potential, clinically actionable, circulating miRNA-based precision medicine pipeline. While it is currently under discussion whether this first step can be taken, we demonstrate here that it is possible to perform classification tasks by exploiting a recursive feature elimination procedure that integrates a heterogeneous ensemble of high-quality, state-of-the-art classifiers on circulating miRNAs. Heterogeneous ensembles can compensate inherent biases of classifiers by using different classification algorithms. Selecting features then further eliminates biases emerging from using data from different studies or batches, yielding more robust and reliable outcomes. The proposed approach is first tested on a tumor classification problem in order to separate 10 different types of cancer, with samples collected over 10 different clinical trials, and later is assessed on a cancer subtype classification task, with the aim to distinguish triple negative breast cancer from other subtypes of breast cancer. Overall, the presented methodology proves to be effective and compares favorably to other state-of-the-art feature selection methods.