本文選題：聚類 + 網絡融合�。� 參考：《中國海洋大學學報(自然科學版)》2017年S1期

【摘要】：近年來隨著人類基因圖譜計劃和癌癥基因圖譜計劃的實施,大量的癌癥數據集體涌現。如何將這些數據有效地集合起來,利用其互補性來區(qū)分癌癥亞型變得尤為重要�，F存在的很多方法大都根據單一的數據類型對癌癥亞型進行聚類,這些方法忽略了數據之間交互影響的信息。相似網絡融合(Similarity Network Fusion(SNF))是一種可以把不同數據類型融合到一起的有效方法,其中構建樣本之間的相似網絡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絡融合(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近鄰方法來構建相似網絡,并將其運用于癌癥亞型聚類。最后用4種癌癥的實驗數據證明了提出的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】： 中國海洋大學數學科學學院;青島市市立醫(yī)院;
【基金】：國家自然科學基金項目(11271341)資助~~
【分類號】：R73-3
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本文編號：1867175