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Massive datasets and machine learning for computational biomedicine: trends and challenges

机译:用于计算生物医学的海量数据集和机器学习:趋势与挑战

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摘要

This survey paper attempts to cover a broad range of topics related to computational biomedicine. The field has been attracting great attention due to a number of benefits it can provide the society with. New technological and theoretical advances have made it possible to progress considerably. Traditionally, problems emerging in this field are challenging from many perspectives. In this paper, we considered the influence of big data on the field, problems associated with massive datasets in biomedicine and ways to address these problems. We analyzed the most commonly used machine learning and feature mining tools and several new trends and tendencies such as deep learning and biological networks for computational biomedicine.
机译:本调查论文试图涵盖与计算生物医学有关的广泛主题。由于该领域可以为社会带来许多好处,因此引起了极大的关注。新的技术和理论上的进步使取得巨大进展成为可能。传统上,从许多角度来看,该领域出现的问题都是具有挑战性的。在本文中,我们考虑了大数据对现场的影响,与生物医学中的海量数据集相关的问题以及解决这些问题的方法。我们分析了最常用的机器学习和特征挖掘工具以及几种新的趋势和趋势,例如深度学习和用于计算生物医学的生物网络。

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  • 来源
    《Annals of Operations Research》 |2019年第2期|5-34|共30页
  • 作者单位

    Univ Florida, Ctr Appl Optimizat, 401 Weil Hall,POB 116595, Gainesville, FL 32611 USA;

    Univ Florida, Ctr Appl Optimizat, 401 Weil Hall,POB 116595, Gainesville, FL 32611 USA|Natl Res Univ Higher Sch Econ, Lab Algorithms & Technol Network Anal, 136 Rodionova, Nizhnii Novgorod 603093, Russia;

    Technol Educ Inst Cent Macedonia, Dept Business Adm, Terma Magnisias 61100, Serres, Greece;

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