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首页> 外文期刊>IEEE/ACM transactions on computational biology and bioinformatics >Relational Analysis of CpG Islands Methylation and Gene Expression in Human Lymphomas Using Possibilistic C-Means Clustering and Modified Cluster Fuzzy Density
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Relational Analysis of CpG Islands Methylation and Gene Expression in Human Lymphomas Using Possibilistic C-Means Clustering and Modified Cluster Fuzzy Density

机译：利用可能的C-均值聚类和修正的聚类模糊密度分析人淋巴瘤中CpG岛甲基化与基因表达的关系

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摘要

Heterogeneous genetic and epigenetic alterations are commonly found in human non-Hodgkin''s lymphomas (NHL). One such epigenetic alteration is aberrant methylation of gene promoter-related CpG islands, where hypermethylation frequently results in transcriptional inactivation of target genes, while a decrease or loss of promoter methylation (hypomethylation) is frequently associated with transcriptional activation. Discovering genes with these relationships in NHL or other types of cancers could lead to a better understanding of the pathobiology of these diseases. The simultaneous analysis of promoter methylation using differential methylation hybridization (DMH) and its associated gene expression using expressed CpG island sequence tag (ECIST) microarrays generates a large volume of methylation-expression relational data. To analyze this data, we propose a set of algorithms based on fuzzy sets theory, in particular possibilistic c-means (PCM) and cluster fuzzy density. For each gene, these algorithms calculate measures of confidence of various methylation-expression relationships in each NHL subclass. Thus, these tools can be used as a means of high volume data exploration to better guide biological confirmation using independent molecular biology methods
机译：异质遗传和表观遗传改变通常在人类非霍奇金淋巴瘤（NHL）中发现。一种这样的表观遗传学改变是基因启动子相关的CpG岛的异常甲基化，其中高甲基化经常导致靶基因的转录失活，而启动子甲基化的减少或丢失（次甲基化）通常与转录激活相关。在NHL或其他类型的癌症中发现具有这些关系的基因可能会导致人们更好地了解这些疾病的病理生物学。使用差异甲基化杂交（DMH）同时分析启动子甲基化及其使用表达的CpG岛序列标签（ECIST）微阵列的相关基因表达可产生大量的甲基化表达相关数据。为了分析这些数据，我们提出了一组基于模糊集理论的算法，特别是可能的c均值（PCM）和聚类模糊密度。对于每个基因，这些算法计算每个NHL子类中各种甲基化-表达关系的置信度。因此，这些工具可以用作大量数据探索的手段，从而更好地指导使用独立分子生物学方法进行的生物学确认

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