本文關(guān)鍵詞： 基因表達(dá)譜 結(jié)直腸癌判別 結(jié)直腸癌分型 特征基因選擇 維數(shù)災(zāi)難 數(shù)據(jù)不平衡性　出處：《南方醫(yī)科大學(xué)》2017年博士論文　論文類型：學(xué)位論文

【摘要】：基于基因表達(dá)譜的癌癥判別指針對(duì)基因表達(dá)譜數(shù)據(jù)集,設(shè)計(jì)有效的分類算法,把正常樣本和癌癥樣本分開,并找出癌癥的判別基因(特征基因);基于基因表達(dá)譜的癌癥分型指針對(duì)基因表達(dá)譜數(shù)據(jù)集,設(shè)計(jì)有效的分類算法,將癌癥樣本分為多個(gè)亞型,并找出判別各個(gè)亞型的特征基因,以利于確定藥物靶向和對(duì)患者的精準(zhǔn)治療。然而,基因表達(dá)譜數(shù)據(jù)集的四大顯著特征:“維數(shù)災(zāi)難、高冗余、高噪聲、數(shù)據(jù)不平衡性”,形成了基于基因表達(dá)譜的癌癥判別與分型的困難。本研究針對(duì)基因表達(dá)譜數(shù)據(jù)集的上述特征,以及結(jié)直腸癌亞型的數(shù)量未知的問題,研究了前沿水平的相關(guān)算法,以此為基礎(chǔ),提出了更合理的結(jié)直腸癌的判別與分型的方法,提高了結(jié)直腸癌判別與分型的準(zhǔn)確性并找出了一系列具有高判別能力的特征基因集。本文分為四部分,第一章為緒論;第二章,研究了基于基因表達(dá)譜的結(jié)直腸癌的判別與分型的相關(guān)算法,主要包括:(1)基于RUSBoost的不平衡數(shù)據(jù)集的分類算法,將該二分類算法擴(kuò)展為多分類算法,命名為 RUSBoost.M2,(2)基于差分進(jìn)化(Differential Evolution,DE)和輪盤搜索策略的特征基因選擇算法DEFSw,并針對(duì)基因表達(dá)譜數(shù)據(jù)的樣本不平衡性,將該算法所封裝的分類評(píng)估測(cè)度和分類算法分別改進(jìn)為權(quán)重精度和RUSBoost.M2算法,提出了 DEFSw.wAcc及DEFSw.RUSBoost.M2.wAcc算法,提升了所選出的特征基因的分類判別能力,(3)將用于視頻監(jiān)控處理的BRPCA(Bayesian Robust Prince Component Analysis)算法作適當(dāng)改進(jìn),引入到基因表達(dá)譜數(shù)據(jù)的處理,用于降維與降噪,(4)基于平面極大過濾圖(Planar Maximally Filtered Graph)的層次信息聚類算法(簡稱DBHT),重點(diǎn)研究了其聚類的原理,利用該算法能自動(dòng)確定類數(shù)并無監(jiān)督地完成聚類的特點(diǎn)來分型;第三章,針對(duì)結(jié)直腸癌的判別,利用第二章所提出的DEFSw.RUSBoost.M2.wAcc特征選擇算法,從TCGA COAD(結(jié)腸癌)數(shù)據(jù)集中選擇并經(jīng)在GEO GSE39582、GSE41657和TCGA READ(直腸癌)數(shù)據(jù)集上驗(yàn)證,篩選出13組只包含1個(gè)基因和88組包含2個(gè)基因、既可高精度地判別結(jié)腸癌也可高精度地判別直腸癌的特征基因集合,以及14組只包含1個(gè)基因、只可高精度地判別結(jié)腸癌的特征基因集合,一些基因之前并無癌癥或結(jié)直腸癌的相關(guān)報(bào)道。同時(shí),對(duì)于5個(gè)已報(bào)道的有前景的結(jié)直腸癌生物標(biāo)志物,均為其找出了多個(gè)輔助基因,能顯著地提高這些生物標(biāo)志物對(duì)結(jié)腸癌的判別能力。第四章,利用TCGA COAD數(shù)據(jù)集,先利用第二章所改進(jìn)的BRPCA算法進(jìn)行基因表達(dá)譜數(shù)據(jù)的降維和降噪,再利用DBHT算法對(duì)BRPCA算法分離出的稀疏成分S進(jìn)行無監(jiān)督聚類,以正常樣本被正確聚類為參照物,將結(jié)腸癌癥分為7個(gè)亞型,然后利用DEFSw.wAcc算法選出了分型的44個(gè)特征基因,其中包含基因MSH6,其是一個(gè)已知的和結(jié)直腸癌遺傳相關(guān)的基因,直接存在于KEGG的結(jié)直腸癌的通路中。
[Abstract]:Cancer discrimination based on gene expression profile refers to the design of an effective classification algorithm for gene expression data sets, which separates normal samples from cancer samples. The cancer classification based on gene expression profile is to design an effective classification algorithm to classify cancer samples into multiple subtypes, and find out the characteristic genes to distinguish each subtype. In order to facilitate the identification of drug targets and accurate treatment of patients. However, the four significant features of the gene expression data set are: "Dimension disaster, high redundancy, high noise, This study aims at the above characteristics of the gene expression profile data set and the unknown number of subtypes of colorectal cancer. Based on the research of relevant algorithms at the frontier level, a more reasonable method for the discrimination and classification of colorectal cancer is proposed. Improve the accuracy of discrimination and classification of rectal cancer and find out a series of characteristic gene sets with high discriminant ability. This paper is divided into four parts, the first chapter is the introduction, the second chapter, In this paper, the related algorithms of discriminating and classifying colorectal cancer based on gene expression profile are studied, including the classification algorithm of RUSBoost based unbalanced dataset, and the two classification algorithms are extended to multi-classification algorithm. Named RUSBoost.M _ 2N _ 2) based on differential evolution evolution (DED) and disk search strategy, the feature gene selection algorithm DEFSw. and the sample imbalance of gene expression profile data. The classification evaluation measure and classification algorithm encapsulated in this algorithm are improved to weight accuracy and RUSBoost.M2 algorithm respectively, and DEFSw.wAcc and DEFSw.RUSBoost.M2.wAcc algorithms are proposed. The BRPCA(Bayesian Robust Prince Component Analysis (BRPCA(Bayesian Robust Prince Component Analysis) algorithm, which is used in video surveillance processing, is introduced to the processing of gene expression profile data. For dimensionality reduction and noise reduction, a hierarchical information clustering algorithm based on Planar Maximally Filtered Graph-based Planar Maximally Filtered Graph-based (DBHT) algorithm is presented, which focuses on the principle of clustering. The algorithm can automatically determine the number of clusters and unsupervised the characteristics of clustering. In chapter 3, according to the discrimination of colorectal cancer, the DEFSw.RUSBoost.M2.wAcc feature selection algorithm proposed in Chapter 2 is used to select the data set of TCGA Coad (Colon Cancer) and verified on GEO GSE39582 GSE41657 and TCGA read (rectal cancer) data set. Thirteen groups contain only one gene and 88 groups contain two genes, which can distinguish the characteristic gene set of colon cancer and rectal cancer with high accuracy, and 14 groups contain only one gene. The characteristic gene sets of colon cancer can only be identified with high accuracy, and some genes have not been previously reported for cancer or colorectal cancer. At the same time, five promising biomarkers for colorectal cancer have been reported. In chapter 4th, using the TCGA COAD data set, the improved BRPCA algorithm was first used to reduce and reduce the noise of the gene expression profile data. Then the sparse component S isolated from BRPCA algorithm is clustered unsupervised by DBHT algorithm, and the normal samples are correctly clustered as reference, and colon cancer is divided into 7 subtypes, and 44 characteristic genes are selected by DEFSw.wAcc algorithm. It contains the gene MSH6, a known genetic gene associated with colorectal cancer, which is directly present in the KEGG pathway for colorectal cancer.
【學(xué)位授予單位】：南方醫(yī)科大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：R735.34

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