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

被引:0
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作者
Anton Kocheturov
Panos M. Pardalos
Athanasia Karakitsiou
机构
[1] University of Florida,Center for Applied Optimization
[2] National Research University Higher School of Economics,Laboratory of Algorithms and Technologies for Network Analysis
[3] Technological Educational Institute of Central Macedonia,Department of Business Administration
来源
关键词
High Dimension Low Sample Size (HDLSS); Feature Mining Technique; Proximal Support Vector Machine (PSVMs); Multiple Imputation By Chained Equations (MICE); Gradient Tree Boosting;
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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.
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页码:5 / 34
页数:29
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