Earticle

초록

영어

The cancer is one of the diseases of serious threat to mankind's health and life nowadays. Resistance to radiation and chemotherapy is a significant obstacle to the treatment of advanced cancer. Gene therapy is a new therapeutic tool for the treatment of diverse types of diseases, including cancer, congenital genetic, infectious diseases. It is well known that the somatic mutation is an important factor that might lead to cancer development. It is difficult to distinguish driver mutations from passenger mutations because of mutational heterogeneity, which is the key to solve and deal with the problem of cancer treatment. In this study, we find an efficient way Multi-Population Genetic Algorithm to solve the maximum weight submatrix problem which is designed to find important mutated driver genes in cancer, then makes the factors comparison of the methods on the simulated and several real mutation datasets. The results show that MPGA performs more efficiently, In addition, MPGA is a most robust method among these approaches, and these important pathways in these cancers are successfully rediscovered. The approach achieved what we expect.

목차

Abstract
 1. Introduction
 2. Methods
  2.1 The Problem
  2.2 Main Process of MPGA
 3. Experiments
  3.1 Simulated Mutation Data
  3.2 Lung Cancer Data
  3.3 Glioblastoma Cancer Data
  3.4 Head and Neck Squamous Cell Carcinoma Data
  3.5 Ovarian Carcinoma Data
 4. Conclusions
 Acknowledgements
 References

키워드

저자

• Shu-Lin Wang [ Institute of College of computer science and electronic engineering Hunan University Changsha, 410082, China ]
• Can-jun Hu [ Institute of College of computer science and electronic engineering Hunan University Changsha, 410082, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Hybrid Information Technology

• 간기

  격월간

• pISSN

  1738-9968

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605