使用自学习和协同训练方法的基于miRNA和基因表达的癌症分类
miRNA and Gene Expression based Cancer Classification using Self- Learning and Co-Training Approaches
摘要：
miRNA和基因表达谱已被证明可用于分类癌症样品。最近已经寻求并开发了有效的分类器。文献中已知使用miRNA/基因表达谱对癌症样品进行分类的许多尝试。然而，半监督学习模型的使用最近已被用于生物信息学，以利用庞大的语料库的公开可用的集。当使用基因和miRNA表达集时，使用标记和未标记集两者来训练样本分类器先前尚未考虑。此外，有动机整合miRNA和基因表达用于半监督癌症分类，因为这提供了更多关于癌症样本特征的信息。在本文中，两个半监督机器学习的方法，即自学习和协同训练，适用于提高癌症样本分类的质量。这些方法利用庞大的公共语料库来丰富训练数据。在自学习中，基于miRNA和基因的分类器被独立地增强。在联合训练中，同时使用miRNA和基因表达谱来提供癌症样本的不同视图。据我们所知，这是首次尝试将这些学习方法应用于癌症分类。使用乳腺癌、肝细胞癌（HCC）和肺癌表达集对这些方法进行了评估。结果表明，在F1的措施，随机森林和SVM分类器的20%的改善。联合训练在乳腺癌的F1测量方面也优于低密度分离（LDS）方法，提高了约25%。
英文摘要：
miRNA and gene expression profiles have been proved useful for classifying cancer samples. Efficient classifiers have been recently sought and developed. A number of attempts to classify cancer samples using miRNA/gene expression profiles are known in literature. However, the use of semi-supervised learning models have been used recently in bioinformatics, to exploit the huge corpuses of publicly available sets. Using both labeled and unlabeled sets to train sample classifiers, have not been previously considered when gene and miRNA expression sets are used. Moreover, there is a motivation to integrate both miRNA and gene expression for a semi-supervised cancer classification as that provides more information on the characteristics of cancer samples. In this paper, two semi-supervised machine learning approaches, namely self-learning and co-training, are adapted to enhance the quality of cancer sample classification. These approaches exploit the huge public corpuses to enrich the training data. In self-learning, miRNA and gene based classifiers are enhanced independently. While in co-training, both miRNA and gene expression profiles are used simultaneously to provide different views of cancer samples. To our knowledge, it is the first attempt to apply these learning approaches to cancer classification. The approaches were evaluated using breast cancer, hepatocellular carcinoma (HCC) and lung cancer expression sets. Results show up to 20% improvement in F1-measure over Random Forests and SVM classifiers. Co-Training also outperforms Low Density Separation (LDS) approach by around 25% improvement in F1-measure in breast cancer.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
