본문 바로가기
HOME> 논문 > 논문 검색상세

학위논문 상세정보

Efficient Analysis of Microarray Gene Expressions 원문보기

  • 저자

    A.K.M. Tauhidul Islam

  • 학위수여기관

    경희대학교

  • 학위구분

    국내석사

  • 학과

    컴퓨터공학과

  • 지도교수

  • 발행년도

    2014

  • 총페이지

    61 p.

  • 키워드

    Microarray Experiments Gene Association Analysis Gene Selection MapReduce;

  • 언어

    eng

  • 원문 URL

    http://www.riss.kr/link?id=T13536622&outLink=K  

  • 초록

    Microarray data analysis has been widely used for extracting relevant biological information from thousands of genes simultaneously expressed in a specific cell. Although many genes are expressed in a sample tissue, most of these are irrelevant or insignificant for clinical diagnosis or disease classification because of missing values and noises. Thus, finding a small, closely related gene set for classification and extracting interesting gene associations are important research problems for microarray analysis. At the same time, scalable methods are required for efficient management of rapidly increasing large volume of microarray data. Moreover, the extracted knowledge should match with publicly available biological resources. In this dissertation, we have first proposed a scalable parallel gene selection method using MapReudce programming model. The proposed method utilizes kNN classifier algorithm for fitness classification. We have used several real and synthetic datasets for experiments. Experiment results show that the proposed method can offer good scalability on large data with increasing number of nodes and can also provide higher classification accuracy rather than using whole gene set. Then, we have explored high utility mining for Gene Association Analysis (GAA) to extract biologically interesting gene associations. We have introduced row enumeration based high utility mining to handle high dimension data. The generated rules are validated with publicly available domain knowledge to signify the effectiveness of the proposed GAA method.


 활용도 분석

  • 상세보기

    amChart 영역
  • 원문보기

    amChart 영역