本文選題：結(jié)直腸癌　切入點(diǎn)：環(huán)境暴露　出處：《西南大學(xué)》2017年碩士論文　論文類型：學(xué)位論文

【摘要】：目前,結(jié)直腸癌(Colorectal Cancer,CRC)已經(jīng)成為世界范圍內(nèi)最常見(jiàn)的惡性腫瘤之一。全世界每年約有120萬(wàn)新發(fā)病例,約60萬(wàn)人死于該疾病。在過(guò)去的幾十年,我國(guó)結(jié)直腸癌發(fā)病率處于較低水平,但近年來(lái),隨著人民生活水平的提高、食物結(jié)構(gòu)、生活方式的改變,人均期望壽命明顯延長(zhǎng),我國(guó)結(jié)直腸癌的發(fā)病率率和死亡率呈逐年上升趨勢(shì)。結(jié)直腸癌除了具有較高的全球發(fā)病率,亦具有較高的死亡率。事實(shí)上,早期的結(jié)直腸癌患者5年生存率可達(dá)90%以上,然而由于其起病較為隱匿,臨床上超過(guò)60%的患者在就診時(shí)已處于中晚期,診斷時(shí)已發(fā)生局部轉(zhuǎn)移,結(jié)直腸癌的5年生存率降至68%,而發(fā)生遠(yuǎn)處轉(zhuǎn)移的結(jié)直腸癌患者年生存率僅為11%。為了診斷和治療結(jié)直腸癌,癌癥科學(xué)家們進(jìn)行了大量的研究,但是到目前為止,其病因及發(fā)病機(jī)制仍然尚未完全明了,雖然大量的流行病學(xué)研究表明結(jié)直腸癌的發(fā)生是一個(gè)復(fù)雜過(guò)程,在這個(gè)過(guò)程中,它不僅會(huì)受到環(huán)境因素、遺傳因素等諸多因素的影響。然而,究竟是哪些環(huán)境因素、遺傳因素對(duì)結(jié)直腸癌的發(fā)生具有一定的影響仍不明確。因此,探索結(jié)直腸癌的致癌因子以及預(yù)測(cè)結(jié)直腸癌的發(fā)病風(fēng)險(xiǎn),對(duì)實(shí)現(xiàn)結(jié)直腸癌早期診斷、早期治療具有重要意義。本文利用生物醫(yī)學(xué)分類、數(shù)據(jù)降維和回歸分析預(yù)測(cè),本文構(gòu)建了一個(gè)多層次結(jié)直腸癌預(yù)測(cè)模型。另外,當(dāng)我們構(gòu)建了多層次預(yù)測(cè)模型之后,我們創(chuàng)新性的提出了一種最大互相關(guān)熵的廣義核遞歸最小二乘算法(Generalized Kernel Recursive Maximum Correntropy Algorithm,GKRMC)的非線性回歸方法來(lái)提高我們對(duì)結(jié)直腸癌的預(yù)測(cè)精度和準(zhǔn)確率。本文的具體工作包括:(1)提出了GKRMC預(yù)測(cè)回歸方法。我們提出了一種非線性的回歸方法GKRMC來(lái)對(duì)模型進(jìn)行回歸分析預(yù)測(cè)。首先介紹了相關(guān)熵的基本概念,從最大相關(guān)熵準(zhǔn)則的理論和核遞歸最小二乘法(Kernel Recursive Lease Squares,KRLS)的基礎(chǔ)上提出了GKRMC算法,使得預(yù)測(cè)模型能更好的訓(xùn)練含有噪聲的樣本,具有較強(qiáng)的抗噪聲的能力。(2)構(gòu)建了一種多層次結(jié)直腸癌預(yù)測(cè)模型。在模型構(gòu)建過(guò)程中,主要通過(guò)三個(gè)模塊介紹整個(gè)流程。分別是:1.生物醫(yī)學(xué)分類;2.數(shù)據(jù)降維;3回歸分析預(yù)測(cè)。首先在生物醫(yī)學(xué)分類的分析中,深度結(jié)合了生物醫(yī)學(xué)知識(shí)對(duì)實(shí)驗(yàn)數(shù)據(jù)進(jìn)行科學(xué)的分類,把數(shù)據(jù)劃分為遺傳信息、人口學(xué)特征、生活方式和食物等四類,從而更接進(jìn)真實(shí)的基因多態(tài)性與環(huán)境因素的區(qū)別。然后建立恰當(dāng)?shù)臄?shù)據(jù)降維模型,進(jìn)一步篩選出與結(jié)直腸癌有關(guān)系并著有顯著性差異的特征。接著說(shuō)明了回歸分析預(yù)測(cè)的流程,將邏輯回歸、支持向量機(jī)、KRLS和GKRMC的預(yù)測(cè)器準(zhǔn)確率度量結(jié)果進(jìn)行對(duì)比分析,來(lái)驗(yàn)證GKRMC算法與傳統(tǒng)的經(jīng)典回歸分析預(yù)測(cè)算法的優(yōu)點(diǎn)以及差異。(3)實(shí)驗(yàn)結(jié)果與分析。試驗(yàn)部分針對(duì)本文提出的算法進(jìn)行了實(shí)現(xiàn)并且對(duì)重要的實(shí)驗(yàn)過(guò)程和數(shù)據(jù)進(jìn)行了強(qiáng)調(diào),同時(shí)進(jìn)行了對(duì)比實(shí)驗(yàn)來(lái)體現(xiàn)GKRMC算法的優(yōu)越性。首先是總結(jié)了生物醫(yī)學(xué)分類結(jié)果,接著展示了數(shù)據(jù)降維后的結(jié)果,最后進(jìn)行橫向方法的比較,證明了GKRMC算法在預(yù)測(cè)結(jié)直腸癌準(zhǔn)確率方面的優(yōu)勢(shì)�；谝陨瞎ぷ魅ヌ剿鳝h(huán)境因素、基因多態(tài)性與結(jié)直腸癌的風(fēng)險(xiǎn)關(guān)聯(lián)。研究結(jié)果表明:(1)環(huán)境因素和遺傳因素在結(jié)直腸癌的發(fā)病中都起著重要的影響;(2)利用我們篩選出來(lái)的這些生物標(biāo)記作為回歸模型的分類器可以準(zhǔn)確和有效的識(shí)別每個(gè)個(gè)體的結(jié)直腸癌風(fēng)險(xiǎn);(3)我們提出的GKRMC算法比傳統(tǒng)的回歸方法具有更好的預(yù)測(cè)能力。
[Abstract]:At present, colorectal cancer (Colorectal, Cancer, CRC) has become one of the most common malignant tumors in the world. There are about 120 new cases worldwide each year, about 600 thousand people died of the disease. In the past few decades, the incidence of colorectal cancer in China is at a low level, but in recent years, with people's life raise the level of food structure, lifestyle changes, the average life expectancy was significantly prolonged in China, colorectal cancer incidence rate and mortality rate increased year by year. In addition to colorectal cancer with high incidence worldwide, also has a high mortality rate. In fact, the early colorectal cancer patients 5 years survival rate is more than 90% however, due to the onset of illness is clinically more than 60% patients have been in advanced stage, diagnosis has metastasis, 5 year survival rate fell to 68% in colorectal cancer, and the occurrence of distant metastasis of colorectal Patients with colorectal cancer year survival rate is only 11%. for the diagnosis and treatment of colorectal cancer, scientists have done a lot of research, but so far, the etiology and pathogenesis is still not completely clear, although a large number of epidemiological studies showed that the occurrence of colorectal cancer is a complicated process, in this process, it will not only be environmental factors, genetic factors and other factors. However, what exactly is the environmental factors, genetic factors on the incidence of colorectal cancer has a certain impact is still not clear. Therefore, to explore the colorectal cancer induced by cancer factor and predict the risk of colorectal cancer development, to realize the early diagnosis of colorectal cancer, early treatment has important significance. In this paper, the use of biomedical classification, dimensionality reduction and regression analysis, this paper constructs a multi-level prediction model of colorectal cancer. In addition, when we build a multi After the hierarchical prediction model, we propose a generalized kernel recursive least squares algorithm for maximum cross-correlation entropy (Generalized Kernel Recursive Maximum Correntropy Algorithm, GKRMC) of colorectal cancer to improve our prediction accuracy and the accuracy of the nonlinear regression method. The contents of this paper include: (1) proposed a regression prediction method GKRMC. We propose a nonlinear GKRMC regression method for regression analysis to forecast model. First introduced the basic concepts of entropy, maximum entropy criterion from the related theory and kernel recursive least square method (Kernel Recursive Lease Squares, KRLS) based on GKRMC algorithm is proposed, which can better forecast model the training samples containing noise, has strong anti noise ability. (2) to construct a multi-level colorectal cancer prediction model. In the model building process Three, mainly through the introduction of the whole process. The module is respectively: 1. biomedical classification; 2. data reduction; 3 regression analysis. First in the analysis of biomedical classification, combined with the depth of the biomedical knowledge scientific classification of the experimental data, the data is divided into genetic information, demographic characteristics, lifestyle and food etc. four, and more close to real difference between gene polymorphisms and environmental factors. Then establish appropriate dimensionality reduction model, further screening characteristics have significant difference and the author and colorectal cancer. Then explains the regression analysis prediction process, the logistic regression, support vector machine, KRLS predictor and GKRMC the accuracy of measurement results were analyzed to verify the GKRMC algorithm and the classical regression algorithm and difference. (3) experimental results and analysis section for the test. The proposed algorithm is implemented and the experiment process and the important data are highlighted, the superiority of the experiment is carried out to reflect the GKRMC algorithm. The first is a summary of the biomedical classification results, then shows the data after dimensionality reduction results, comparing the horizontal method, it is proved that the GKRMC algorithm accuracy rate the advantage in prediction of colorectal cancer. To explore environmental factors based on the above work, the risk associated gene polymorphism and colorectal cancer. The results show that: (1) environmental factors and genetic factors plays an important role in the pathogenesis of colorectal cancer; (2) we use screened these biomarkers as regression model the classifier can accurately and effectively identify the individual risk of colorectal cancer; (3) prediction ability of our proposed GKRMC algorithm is better than the traditional regression method.

【學(xué)位授予單位】：西南大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2017
【分類號(hào)】：R735.3;O212.1

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