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摘要翻译：
随着测序技术的发展，每天都会产生大量的基因组学数据。如果不对这些数据进行适当的分析，这些数据就没有价值。从这些原始数据中提取有用的信息需要不同种类的分析。分类、预测、聚类和模式提取是数据挖掘的重要技术。这些技术要求适当地选择数据属性以获得准确的结果。然而，生物信息学数据是高维的，通常有数百个属性。如此大量的属性影响了用于分类/预测的机器学习算法的性能。因此，需要降维技术来减少可以进一步用于分析的属性的数量。本文采用主成分分析法和因子分析法对生物信息学数据进行降维。将这些技术应用于白血病数据集，使属性数由原来的减少到原来的。
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英文标题：
《Using PCA and Factor Analysis for Dimensionality Reduction of
Bio-informatics Data》
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作者：
M. Usman Ali, Shahzad Ahmed, Javed Ferzund, Atif Mehmood, Abbas Rehman
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最新提交年份：
2017
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分类信息：
一级分类：Quantitative Biology 数量生物学
二级分类：Other Quantitative Biology 其他定量生物学
分类描述：Work in quantitative biology that does not fit into the other q-bio classifications
不适合其他q-bio分类的定量生物学工作
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一级分类：Computer Science 计算机科学
二级分类：Computational Engineering, Finance, and Science 计算工程、金融和科学
分类描述：Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
涵盖了计算机科学在科学、工程和金融领域复杂系统的数学建模中的应用。这里的论文是跨学科和面向应用的，集中在技术和工具，使挑战性的计算模拟能够执行，其中往往需要使用超级计算机或分布式计算平台。包括ACM学科课程J.2、J.3和J.4（经济学）中的材料。
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英文摘要：
Large volume of Genomics data is produced on daily basis due to the advancement in sequencing technology. This data is of no value if it is not properly analysed. Different kinds of analytics are required to extract useful information from this raw data. Classification, Prediction, Clustering and Pattern Extraction are useful techniques of data mining. These techniques require appropriate selection of attributes of data for getting accurate results. However, Bioinformatics data is high dimensional, usually having hundreds of attributes. Such large a number of attributes affect the performance of machine learning algorithms used for classification/prediction. So, dimensionality reduction techniques are required to reduce the number of attributes that can be further used for analysis. In this paper, Principal Component Analysis and Factor Analysis are used for dimensionality reduction of Bioinformatics data. These techniques were applied on Leukaemia data set and the number of attributes was reduced from to.
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PDF链接：
https://arxiv.org/pdf/1707.07189
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