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

被引:22
|
作者
Kocheturov, Anton [1 ]
Pardalos, Panos M. [1 ,2 ]
Karakitsiou, Athanasia [3 ]
机构
[1] Univ Florida, Ctr Appl Optimizat, 401 Weil Hall,POB 116595, Gainesville, FL 32611 USA
[2] Natl Res Univ Higher Sch Econ, Lab Algorithms & Technol Network Anal, 136 Rodionova, Nizhnii Novgorod 603093, Russia
[3] Technol Educ Inst Cent Macedonia, Dept Business Adm, Terma Magnisias 61100, Serres, Greece
关键词
COMPUTER-AIDED DETECTION; NEAR-INFRARED SPECTROSCOPY; CLINICAL NEUROPHYSIOLOGY; PROSTATE-CANCER; NEURAL-NETWORKS; PATTERN-RECOGNITION; FEATURE-SELECTION; SEIZURE DETECTION; RNA NETWORK; HEALTH-CARE;
D O I
10.1007/s10479-018-2891-2
中图分类号
C93 [管理学]; O22 [运筹学];
学科分类号
070105 ; 12 ; 1201 ; 1202 ; 120202 ;
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.
引用
收藏
页码:5 / 34
页数:30
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