北京生物醫(yī)學(xué)工程

一種面向組學(xué)數(shù)據(jù)的中級融合分類方法

Amid-level fusion method for omics dataset

作者：李明達鄭浩然

單位：中國科學(xué)技術(shù)大學(xué)計算機科學(xué)與技術(shù)學(xué)院(合肥230027)

關(guān)鍵詞：學(xué)數(shù)據(jù);降維;中級融合;偏最小二乘法;支持向量機

分類號：R318.04

出版年·卷·期（頁碼）：2016·35·3（249-253）

摘要：

目的對組學(xué)數(shù)據(jù)進行深入分析有助于推動醫(yī)療診斷等方面的研究。利用單一種類組學(xué)數(shù)據(jù)的分析方法無法解決某些復(fù)雜生物醫(yī)學(xué)問題。為利用多種組學(xué)信息以解決復(fù)雜的生物醫(yī)療問題，本文提出一種中級融合分類方法。方法引入偏最小二乘法(partial least squares,PLS)分別對各種組學(xué)數(shù)據(jù)進行降維，然后利用支持向量機（support vector machine,SVM）對融合后的數(shù)據(jù)進行分類。結(jié)果 “非小細胞肺癌與腎癌”和“結(jié)腸直腸癌與結(jié)腸直腸腺瘤”這兩個組學(xué)數(shù)據(jù)集被用于測試本文方法的有效性。在這兩個癌癥組學(xué)數(shù)據(jù)集上的應(yīng)用，體現(xiàn)出該方法不但能有效降低高維組學(xué)數(shù)據(jù)的維數(shù)，而且具有較高的分類準(zhǔn)確率（接受者操作特征曲線下的面積達0.95以上）。結(jié)論本文提出的中級融合方法能夠利用多種組學(xué)數(shù)據(jù)對癌癥樣本進行分類，可有效提高疾病診斷的準(zhǔn)確率。

Objective The analysis of omics data is of great importance for medical diagnosis. Methods to analyze only one type of omic dataset cannot solve certain complex biomedical problems. In order to solve the complex biomedical problems by using different kinds of omics datasets,a mid-level fusion method is proposed. Methods Partial least squares (PLS) is used to reduce the dimension,then support vector machine (SVM) is used for classification. Results “Non-small cell lung cancer vs renal cancer” and “colorectal cancer vs colorectal adenomas” datasets are used for testing the method’s effectiveness. The experimental results demonstrate that the mid-level fusion method can not only reduce the dimension of omics data but also obtain a high classification accuracy (The area under receiver operating characteristic curve is higher than 0.95). Conclusions The mid-level fusion method takes advantages of different kinds of omics datasets for classification and improves the accuracy of diagnosis.

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