Mutated Driver Pathways In Cancer Research Articles

An Efficient Exact Method For Identifying Mutated Driver Pathways In Cancer

One of the key challenges in bioinformatics is to find the distinction between driver mutations that lead to tumorigenesis and random passenger mutations, which are known to be neutral and does not play any role in cancer development. To solve this problem, several approaches and techniques were used. In this article, we introduce an algorithm based on an exact approach in order to solve the so called “maximum weight submatrix problem”. The proposed algorithm maximizes the weight and samples all the possible pathways that can be found. We present the details about our algorithm and then compare them to a metaheuristic algorithm called Genetic Algorithm (GA) and with another exact model named Binary Linear Programming (BLP). Our Exact Algorithm shows good results in terms of maximizing the weight and finding all possible pathways.

The Discovery of Mutated Driver Pathways in Cancer: Models and Algorithms.

The pathogenesis of cancer in human is still poorly understood. With the rapid development of high-throughput sequencing technologies, huge volumes of cancer genomics data have been generated. Deciphering that data poses great opportunities and challenges to computational biologists. One of such key challenges is to distinguish driver mutations, genes as well as pathways from passenger ones. Mutual exclusivity of gene mutations (each patient has no more than one mutation in the gene set) has been observed in various cancer types and thus has been used as an important property of a driver gene set or pathway. In this article, we aim to review the recent development of computational models and algorithms for discovering driver pathways or modules in cancer with the focus on mutual exclusivity-based ones.

Multi-Population Genetic Algorithm for Identifying Mutated Driver Pathways in Cancer

Multi-Population Genetic Algorithm for Identifying Mutated Driver Pathways in Cancer