论文标题
使用非阴性基质分解对癌症微阵列和DNA甲基化数据的预测
Prediction of Cancer Microarray and DNA Methylation Data using Non-negative Matrix Factorization
论文作者
论文摘要
在过去的几年中，微阵列技术在许多生物学模式中都有相当大的传播，特别是在与白血病，前列腺癌，结肠癌等有关的生物学疾病中，尤其是那些与癌症疾病相关的。这项研究旨在提出不同的算法和方法，以降低此类微阵列数据集的维度。这项研究利用了此类微阵列数据的基质样结构，并使用一种称为非负基质分解（NMF）的流行技术来降低维度，主要是在生物学数据领域。然后比较这些算法的分类精度。该技术的精度为98％。
Over the past few years, there has been a considerable spread of microarray technology in many biological patterns, particularly in those pertaining to cancer diseases like leukemia, prostate, colon cancer, etc. The primary bottleneck that one experiences in the proper understanding of such datasets lies in their dimensionality, and thus for an efficient and effective means of studying the same, a reduction in their dimension to a large extent is deemed necessary. This study is a bid to suggesting different algorithms and approaches for the reduction of dimensionality of such microarray datasets. This study exploits the matrix-like structure of such microarray data and uses a popular technique called Non-Negative Matrix Factorization (NMF) to reduce the dimensionality, primarily in the field of biological data. Classification accuracies are then compared for these algorithms. This technique gives an accuracy of 98%.
