원문정보
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
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