A New Maximum Weight Submatrix Solver Tool for Identifying Mutated Driver Pathways in Cancer using Python

Cancer may be a title given to all kind of infections caused by hereditary changes, such as substantial changes in DNA. Cancers are caused by mutations-which occur during cell division-that may possibly be inherited, provoked by environmental determinants, or result from some random DNA replication errors which simply maybe considered as bad luck. Recently in bioinformatics, one of the most heated topics is to search about the separation between driver mutations that lead to cancer and passenger mutations, which are well noted to be neutral and do not have any part in this disease development. Indeed, to solve this problem, several approaches and techniques were used in the literature. The main aim of this work is to show a new computation tool called “The Maximum Weight Submatrix Solver (MWS Solver)” using Python language. In fact, we programmed three metaheuristics called: Genetic Algorithm, Evolutionary Programming and Random Optimization Algorithm, as an approximate approach in order to maximize the weight and sample all the possible driver pathways that can be found. A comparison of these approximate methods with other previous exact and random methods has also been taken into consideration. The experimental tests demonstrate that the proposed algorithms implemented via MWS Solver may become a useful open source and free complementary instrument for finding cancer pathways.