本文選題：云模型理論　切入點(diǎn)：粒子群優(yōu)化算法　出處：《吉林大學(xué)》2012年碩士論文

【摘要】：隨著科學(xué)技術(shù)的不斷發(fā)展與后基因組時(shí)代的來臨，人類對(duì)基因的了解越發(fā)深入，同時(shí)也大大提高了基因表達(dá)數(shù)據(jù)的檢測(cè)手段與檢測(cè)技術(shù)，使研究人員能在較短的時(shí)間與較少的實(shí)驗(yàn)次數(shù)下獲得大量的基因表達(dá)數(shù)據(jù)。這些數(shù)據(jù)對(duì)于研究各種疾病的發(fā)病機(jī)理、疾病的診斷、以及開發(fā)新型藥物和對(duì)疾病在基因水平進(jìn)行基因治療都具有重要意義。 癌癥是影響人類健康的主要疾病。在基因水平上對(duì)癌癥相關(guān)基因進(jìn)行分類和預(yù)測(cè)研究是了解癌癥的發(fā)病機(jī)理，找到基因表達(dá)數(shù)據(jù)的變化與癌癥病理特征之間的關(guān)系，從而開發(fā)出針對(duì)特定基因的新型藥物對(duì)癌癥進(jìn)行治療的關(guān)鍵步驟。然而在海量的基因數(shù)據(jù)中只有較少的樣本可以進(jìn)行研究分析，這就造成了嚴(yán)重的“維災(zāi)”現(xiàn)象，同時(shí)因?yàn)榘┌Y基因數(shù)據(jù)中的大量沉余，導(dǎo)致了分類性能和準(zhǔn)確性的嚴(yán)重下降。為了解決上述問題，本文將使用基于云模型的分類器對(duì)癌癥相關(guān)基因進(jìn)行分類研究，，目前應(yīng)用云模型理論對(duì)癌癥相關(guān)基因進(jìn)行分類的相關(guān)文獻(xiàn)尚不多見，本文意在利用云模型理論在數(shù)據(jù)挖掘方面的優(yōu)勢(shì)，結(jié)合粒子群優(yōu)化算法，對(duì)癌癥相關(guān)基因進(jìn)行分類預(yù)測(cè)研究。 本文主要工作如下： （1）詳細(xì)對(duì)生物信息學(xué)（bioinformatics）進(jìn)行總結(jié)與闡述，包括生物信息學(xué)（bioinformatics）的定義、產(chǎn)生與發(fā)展、研究領(lǐng)域和近期研究的主要成就等。 （2）對(duì)當(dāng)前生物信息學(xué)研究的熱點(diǎn)問題——癌癥相關(guān)基因的分類與預(yù)測(cè)問題進(jìn)行分析與研究。主要包括癌癥的發(fā)生和發(fā)展與細(xì)胞周期之間的規(guī)律、本文所應(yīng)用的數(shù)據(jù)集中的特征基因及其生物學(xué)意義以及國內(nèi)外癌癥相關(guān)基因的研究進(jìn)展情況。 （3）對(duì)云模型理論進(jìn)行詳細(xì)的闡述，包括云的定義、云模型的基本特點(diǎn)和云模型的三個(gè)基本數(shù)字特征。分析與討論了云模型的發(fā)生器及其相關(guān)算法，并對(duì)近幾十年來云模型理論的研究進(jìn)展情況進(jìn)行介紹。 （4）將粒子群優(yōu)化算法與云模型理論相結(jié)合，應(yīng)用云模型分類器對(duì)癌癥相關(guān)基因進(jìn)行分類預(yù)測(cè)研究，將基于云模型的分類器與其他有類似功能的分類方法進(jìn)行比較研究，分析各自的優(yōu)缺點(diǎn)，并提出改進(jìn)方案。同時(shí)分析研究各種應(yīng)用不同算法的云分類器在分類效果與分類效率上的不同，對(duì)其進(jìn)行比較，驗(yàn)證了基于粒子群云模型癌癥相關(guān)基因分類預(yù)測(cè)的有效性。
[Abstract]:With the continuous development of science and technology and the advent of post-genome era, the understanding of genes has become more and more in-depth, and the detection methods and techniques of gene expression data have also been greatly improved.This allows researchers to obtain large amounts of gene expression data in a shorter time and fewer experiments.These data are of great significance for the study of the pathogenesis and diagnosis of various diseases, as well as the development of new drugs and gene therapy for diseases at the gene level.Cancer is a major disease affecting human health.Classification and prediction of cancer-related genes at the gene level is to understand the pathogenesis of cancer and to find out the relationship between the changes of gene expression data and the pathological characteristics of cancer.A key step in cancer treatment is to develop new drugs for specific genes.However, only a small number of samples can be studied and analyzed in a large amount of genetic data, which results in a serious "disaster of maintenance" phenomenon. At the same time, because of the large amount of residual in cancer gene data, the classification performance and accuracy are seriously reduced.In order to solve the above problems, this paper will use cloud model-based classifier to classify cancer related genes. At present, there are few literatures about cancer related genes classification based on cloud model theory.This paper aims to make use of the advantage of cloud model theory in data mining, combining with particle swarm optimization algorithm, to study the classification and prediction of cancer related genes.The main work of this paper is as follows:1) summarize and expound bioinformatics in detail, including the definition, production and development of bioinformatics, the research field and the main achievements of recent research, etc.This paper analyzes and studies the classification and prediction of cancer related genes, which is a hot topic in bioinformatics.It mainly includes the regularity between the occurrence and development of cancer and cell cycle, the characteristic genes and their biological significance in the data set used in this paper, and the research progress of cancer related genes at home and abroad.3) the theory of cloud model is expounded in detail, including the definition of cloud, the basic characteristics of cloud model and the three basic numerical features of cloud model.The generator of cloud model and its related algorithms are analyzed and discussed, and the research progress of cloud model theory in recent decades is introduced.4) combining particle swarm optimization algorithm with cloud model theory, applying cloud model classifier to classify and predict cancer related genes, comparing the classifier based on cloud model with other classification methods with similar functions.The advantages and disadvantages of each are analyzed, and the improvement scheme is put forward.At the same time, the different classification effects and classification efficiency of different cloud classifiers with different algorithms are analyzed and compared to verify the effectiveness of the classification prediction of cancer related genes based on particle swarm cloud model.
【學(xué)位授予單位】：吉林大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2012
【分類號(hào)】：TP3;R730.2

【參考文獻(xiàn)】

相關(guān)期刊論文 前10條

1 杜益鳥,宋自林,李德毅;基于云模型的關(guān)聯(lián)規(guī)則挖掘方法[J];解放軍理工大學(xué)學(xué)報(bào)(自然科學(xué)版);2000年01期

2 李德毅，孟海軍，史雪梅;隸屬云和隸屬云發(fā)生器[J];計(jì)算機(jī)研究與發(fā)展;1995年06期

3 陽春華;谷麗姍;桂衛(wèi)華;;自適應(yīng)變異的粒子群優(yōu)化算法[J];計(jì)算機(jī)工程;2008年16期

4 楊朝暉,李德毅;二維云模型及其在預(yù)測(cè)中的應(yīng)用[J];計(jì)算機(jī)學(xué)報(bào);1998年11期

5 呂輝軍,王曄,李德毅,劉常昱;逆向云在定性評(píng)價(jià)中的應(yīng)用[J];計(jì)算機(jī)學(xué)報(bào);2003年08期

6 高海兵;周馳;高亮;;廣義粒子群優(yōu)化模型[J];計(jì)算機(jī)學(xué)報(bào);2005年12期

7 李寧;孫德寶;鄒彤;秦元慶;尉宇;;基于差分方程的PSO算法粒子運(yùn)動(dòng)軌跡分析[J];計(jì)算機(jī)學(xué)報(bào);2006年11期

8 于繁華;劉寒冰;戴金波;;求解多目標(biāo)優(yōu)化問題的灰色粒子群算法[J];計(jì)算機(jī)應(yīng)用;2006年12期

9 徐燕娟;李眾;張日勛;;一維多規(guī)則正態(tài)云模型映射器的算法研究[J];科學(xué)技術(shù)與工程;2010年01期

10 杜瀊,李德毅;基于云的概念劃分及其在關(guān)聯(lián)采掘上的應(yīng)用[J];軟件學(xué)報(bào);2001年02期

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