The Research Of Hybrid Algorithm For Identifying Mutated Driver Pathways In Cancer

As we all know,cancer is a very complex disease,and it is very difficult to cure so far.Cancer may be caused by gradually accumulated mutations when DNA is replicated which is gene aberrations.In general,there are two types of genetic aberrations,one type is neutral to cancer proliferation;the other can promote the cancer cell to proliferate infinitely and diffuse.It is the key that understanding the molecular mechanisms of cancer progression in identifying driving mutations,driving genes,or driving pathways.In massive amounts of data and mutations,there is a lot of noise,and these noises are an uncertain factor in interpreting and analyzing data.One of great challenge is how to select informative genes which are functional driver genes and filter out useless genes that have few influences on the development of cancer in cancer genomics.Therefore,the identification of driving pathway has become a hot research in bioinformatics and medical field.This paper focuses on the genetic matrix model to identify the driving pathways in maximum weighted matrix.(1)An algorithm combining dynamic ant colony algorithm with genetic algorithm(Dynamically heuristic ant colony optimization and genetic algorithm,DACGA)is proposed.Genetic algorithm has the ability of strong robustness with the potential of parallelism,and has the advantages of rapidity,randomness,expansibility.Ant colony optimization with the characteristics of parallelism,positive feedback and high accuracy makes the use of feedback by learning.To avoid falling into local optimal solution,we combine two algorithms,and optimize the fitness function,crossover operation,mutation operation.The proposed algorithm is evaluated on simulated and biological datasets,respectively,and the experimental results indicate that the robustness and accuracy of the algorithm can be improved in a certain extent,and it can identify more meaningful driving paths in biological sense than other algorithms.(2)For the DACGA algorithm,when the sample is very large,the process of finding the maximum weighted matrix is time-consuming.Therefore,it is necessary to propose artificial fish swarm algorithm and genetic algorithm(AFSGA)to solve this problem.In the first step,compute the number of mutation of every gene,and cut genes if the value less than threshold.In second step,create networked genes by property of high exclusivity.In the last step,we use AFSGA for discovering driver pathways.The experimental results obtained on simulation and biological data indicate that our method outperforms other methods in computing cost and discovering driver pathways.Our method can identify not only known pathways,but also some novel genes which have not been discovered in prior methods.