Background: Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors.
Classifying gene expression data is known to contain keys for solving the fundamental problems in cancer studies. However, this issue is a complex task because of the large p, small n issue on gene expression data analysis. In this paper, we propose the improvements in the large p, small n classification issue for the study of human cancer. First, a new enhancing sample size method with generative adversarial network is proposed to improve classification algorithms. Second, we suggest a classification approach with over-sampling technique using features extracted by deep convolutional neural network. Numerical test results on fifty very-high-dimensional and low-sample-size gene expression data datasets from the Kent Ridge Biomedical and Array Expression repositories illustrate that the proposed models are more accurate than state-of-the-art classifying models. In addition, we also have explored the performance of support vector machines, k nearest neighbors and random forests, which have improved when apply our approaches.
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