本文選題：聚類 + 網(wǎng)絡(luò)融合　； 參考：《中國(guó)海洋大學(xué)學(xué)報(bào)(自然科學(xué)版)》2017年S1期

【摘要】：近年來(lái)隨著人類基因圖譜計(jì)劃和癌癥基因圖譜計(jì)劃的實(shí)施,大量的癌癥數(shù)據(jù)集體涌現(xiàn)。如何將這些數(shù)據(jù)有效地集合起來(lái),利用其互補(bǔ)性來(lái)區(qū)分癌癥亞型變得尤為重要。現(xiàn)存在的很多方法大都根據(jù)單一的數(shù)據(jù)類型對(duì)癌癥亞型進(jìn)行聚類,這些方法忽略了數(shù)據(jù)之間交互影響的信息。相似網(wǎng)絡(luò)融合(Similarity Network Fusion(SNF))是一種可以把不同數(shù)據(jù)類型融合到一起的有效方法,其中構(gòu)建樣本之間的相似網(wǎng)絡(luò)是該方法的重要步驟之一。本文提出包含鄰居信息的相似網(wǎng)絡(luò)融合(Neighborhood-Information-Embedded Similarity Network Fusion(NSNF))方法,用包含鄰居信息的多重緊密k近鄰方法代替原有的k近鄰方法來(lái)構(gòu)建相似網(wǎng)絡(luò),并將其運(yùn)用于癌癥亞型聚類。最后用4種癌癥的實(shí)驗(yàn)數(shù)據(jù)證明了提出的NSNF方法比傳統(tǒng)的SNF方法在聚類性能上有了很大的提高。
[Abstract]:In recent years, with the implementation of the human gene mapping program and the cancer gene mapping program, a large number of cancer data have emerged. How to effectively aggregate these data and make use of their complementarities to distinguish cancer subtypes is particularly important. There are many existing methods to cluster cancer subtypes according to a single data type. These methods ignore the information of data interaction. Similarity Network fusion is an effective method to fuse different data types together, and the construction of similar networks between samples is one of the important steps of this method. In this paper, a similar network fusion method containing neighbor information is proposed. The method of neighbor information is used to construct the similar network instead of the original k-nearest neighbor method, and it is applied to cancer subtype clustering. Finally, the experimental data of four kinds of cancers show that the proposed NSNF method is better than the traditional SNF method in clustering performance.
【作者單位】： 中國(guó)海洋大學(xué)數(shù)學(xué)科學(xué)學(xué)院;青島市市立醫(yī)院;
【基金】：國(guó)家自然科學(xué)基金項(xiàng)目(11271341)資助~~
【分類號(hào)】：R73-3
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本文編號(hào)：1867175