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

【摘要】：隨著科學技術的不斷發(fā)展與后基因組時代的來臨，人類對基因的了解越發(fā)深入，同時也大大提高了基因表達數(shù)據(jù)的檢測手段與檢測技術，使研究人員能在較短的時間與較少的實驗次數(shù)下獲得大量的基因表達數(shù)據(jù)。這些數(shù)據(jù)對于研究各種疾病的發(fā)病機理、疾病的診斷、以及開發(fā)新型藥物和對疾病在基因水平進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。在基因水平上對癌癥相關基因進行分類和預測研究是了解癌癥的發(fā)病機理，找到基因表達數(shù)據(jù)的變化與癌癥病理特征之間的關系，從而開發(fā)出針對特定基因的新型藥物對癌癥進行治療的關鍵步驟。然而在海量的基因數(shù)據(jù)中只有較少的樣本可以進行研究分析，這就造成了嚴重的“維災”現(xiàn)象，同時因為癌癥基因數(shù)據(jù)中的大量沉余，導致了分類性能和準確性的嚴重下降。為了解決上述問題，本文將使用基于云模型的分類器對癌癥相關基因進行分類研究，，目前應用云模型理論對癌癥相關基因進行分類的相關文獻尚不多見，本文意在利用云模型理論在數(shù)據(jù)挖掘方面的優(yōu)勢，結合粒子群優(yōu)化算法，對癌癥相關基因進行分類預測研究。 本文主要工作如下： （1）詳細對生物信息學（bioinformatics）進行總結與闡述，包括生物信息學（bioinformatics）的定義、產生與發(fā)展、研究領域和近期研究的主要成就等。 （2）對當前生物信息學研究的熱點問題——癌癥相關基因的分類與預測問題進行分析與研究。主要包括癌癥的發(fā)生和發(fā)展與細胞周期之間的規(guī)律、本文所應用的數(shù)據(jù)集中的特征基因及其生物學意義以及國內外癌癥相關基因的研究進展情況。 （3）對云模型理論進行詳細的闡述，包括云的定義、云模型的基本特點和云模型的三個基本數(shù)字特征。分析與討論了云模型的發(fā)生器及其相關算法，并對近幾十年來云模型理論的研究進展情況進行介紹。 （4）將粒子群優(yōu)化算法與云模型理論相結合，應用云模型分類器對癌癥相關基因進行分類預測研究，將基于云模型的分類器與其他有類似功能的分類方法進行比較研究，分析各自的優(yōu)缺點，并提出改進方案。同時分析研究各種應用不同算法的云分類器在分類效果與分類效率上的不同，對其進行比較，驗證了基于粒子群云模型癌癥相關基因分類預測的有效性。
[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.
【學位授予單位】：吉林大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TP3;R730.2

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