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首页> 外文会议>IEEE International Conference on Bioinformatics and Biomedicine >Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer
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Iterative segmented least square method for functional microRNA-mRNA module discovery in breast cancer

机译：乳腺癌中功能性microRNA-mRNA模块发现的迭代分段最小二乘方法

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MicroRNAs (miRNAs) have significant biological roles at the molecular level by regulating genes post-transcriptionally. To understand the functional effects of miRNAs in different biological contexts, it is essential to elucidate miRNA-mRNA regulatory modules (MRMs). The computational complexity for inferencing MRMs is very high due to the many-to-many relationships between miRNAs and mRNAs and inferencing MRMs is still a challenging unresolved problem. In this paper, we propose a novel iterative segmented least square method for functional MRM discovery. Our method operates in two steps: 1) grouping and ordering the miRNAs and mRNAs to build per-sample matrices representing miRNA-mRNA regulations, and 2) determining maximum sized modules from structured miRNA-mRNA matrices. In experiments with human breast cancer data sets from TCGA, we show that our method outperforms existing methods in terms of both GO similarity and cluster evaluation. In addition, we show that modules determined by our method can be used for breast cancer survival prediction and subtype classification.
机译：通过在转录后调控基因，MicroRNA（miRNA）在分子水平上具有重要的生物学作用。要了解miRNA在不同生物学环境中的功能作用，阐明miRNA-mRNA调节模块（MRM）至关重要。由于miRNA和mRNA之间存在多对多关系，因此推断MRM的计算复杂度非常高，而推断MRM仍然是一个有挑战性的未解决问题。在本文中，我们提出了一种新颖的迭代分段最小二乘方法，用于功能性MRM发现。我们的方法分两个步骤进行：1）对miRNA和mRNA进行分组和排序以构建代表miRNA-mRNA法规的按样本矩阵，以及2）从结构化的miRNA-mRNA矩阵确定最大大小的模块。在来自TCGA的人类乳腺癌数据集的实验中，我们表明，就GO相似度和聚类评估而言，我们的方法优于现有方法。此外，我们表明，通过我们的方法确定的模块可用于乳腺癌生存预测和亚型分类。

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