An Analysis of Breast Cancer Gene Sequences using Differential Evaluation.

In the present age of innovation the medicinal field has emerged as a most vital areas of research with cancer being a significant topic, where real treatment has not been found yet. Cancer diseases must be diagnosed at an early stage to increase survival rate. Breast cancer is a leading cause of death mostly among women worldwide. Soft Computing and artificial intelligence provide methodologies for the early detection of breast cancer tumors due to their capabilities to handle complex, large and noisy proteomic and genomic data sets. Differential Evolution (DE) optimization algorithms are proposed here to determine the optimal treatment set based on available data. Worst treatment results are removed from each optimization stage and randomly generated new treatment routes are added in each step to find an approximate optimal solution to the given data set. In the present paper this methodology is implemented on breast cancer and normal breast genomic data sets to generate best average and best value for each generation in the optimization algorithm. These values can be used in further diagnosis and analysis. [ABSTRACT FROM AUTHOR]

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