基于人皮肤成纤维细胞转录组数据的乳腺癌相关基因的信息理论框架
An Information-Theoretic Framework for Identifying Age-Related Genes Using Human Dermal Fibroblast Transcriptome Data
摘要：
对年龄相关基因的研究对于多种目的都非常重要，例如，提高我们对衰老机制的理解，增加预期寿命，年龄预测和其他医疗保健应用。在他的工作中，从一组27，142个基因开始，我们开发了一个信息理论框架，通过对人类皮肤成纤维细胞基因表达数据应用无监督和半监督学习技术来识别与衰老相关的基因。首先，我们使用无监督学习和应用信息理论的措施，以确定关键功能的有效表示的基因表达值的转录组数据。使用识别的特征，我们对数据进行聚类。最后，我们使用不同的距离度量对聚类进行半监督学习，以识别可能与衰老相关的新基因。无监督和半监督方法的性能评估表明该框架的有效性。
英文摘要：
Investigation of age-related genes is of great importance for multiple purposes, for instance, improving our understanding of the mechanism of ageing, increasing life expectancy, age prediction, and other healthcare applications. In his work, starting with a set of 27,142 genes, we develop an information-theoretic framework for identifying genes that are associated with aging by applying unsupervised and semi-supervised learning techniques on human dermal fibroblast gene expression data. First, we use unsupervised learning and apply information-theoretic measures to identify key features for effective representation of gene expression values in the transcriptome data. Using the identified features, we perform clustering on the data. Finally, we apply semi-supervised learning on the clusters using different distance measures to identify novel genes that are potentially associated with aging. Performance assessment for both unsupervised and semi-supervised methods show the effectiveness of the framework.
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