在输入的基因表达数据集上识别最重要的基因

2015 Fifth International Conference on Communication Systems and Network Technologies Pub Date : 2015-04-04 DOI:10.1109/CSNT.2015.187

Pranoti R. Kamble, Rakhi D. Wajgi, M. Kshirsagar

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引用次数: 0

摘要

由于微阵列技术领域的深入研究，产生了大量的基因表达。这些基因数据集有助于更深入地了解细胞及其功能。在捕获基因表达的同时，数据集中也加入了噪声。为了下游分析的准确性，有必要对这些数据进行预处理。这有助于准确地识别最重要的和共同表达的基因。在本文中，我们在应用归一化和离散化后实现了USQR算法的数据约简。我们使用了包含1700个基因的血清数据集。

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Identifying Most Significant Genes on Imputed Gene Expression Dataset

Due to advance research in the field of Microarray technology large amount of gene expressions are generated. These gene dataset helps in getting more insight of cells and their functioning. While capturing gene expressions noise get added in the dataset. For the accuracy of downstream analysis it is necessary to preprocess this data. This helps in accurately identifying most significant and co-expressing genes. In this paper, we have implemented USQR algorithm for data reduction after applying normalization and discretization. We have used serum dataset containing 1700 genes.

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2015 Fifth International Conference on Communication Systems and Network Technologies

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