Data Mining in Cancer Research

被引：23

作者：

Lisboa, Paulo J. G. ^{[1
]}

Vellido, Alfredo ^{[2
]}

Tagliaferri, Roberto ^{[3
]}

Napolitano, Francesco ^{[3
]}

Ceccarelli, Michele ^{[4
]}

Martin-Guerrero, Jose D. ^{[5
]}

Biganzoli, Elia ^{[6
]}

机构：

[1] Liverpool John Moores Univ, Liverpool L3 5UX, Merseyside, England

[2] Tech Univ Catalonia, Girona, Spain

[3] Univ Salerno, Salerno, Italy

[4] Univ Sannio, Benevento, Italy

[5] Univ Valencia, E-46003 Valencia, Spain

[6] Univ Milan, I-20122 Milan, Italy

来源：

IEEE COMPUTATIONAL INTELLIGENCE MAGAZINE | 2010年 / 5卷 / 01期

关键词：

GENE-EXPRESSION DATA; REGULATORY NETWORKS; CLUSTER-ANALYSIS; IDENTIFICATION; BIOLOGY; MODELS; BRAIN;

D O I：

10.1109/MCI.2009.935311

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Advances in cancer medicine have traditionally come from detailed understanding of biological processes, later translated into therapeutic interventions, whose effectiveness is established by rigorous analysis of clinical trials. Over the last two decades the increasing throughput of data from microarray screening, spectral imaging and longitudinal studies are turning the understanding of cancer pathology into as much a data-based as a biologically and clinically driven science, with potential to impact more strongly on evidence-based decision support moving towards personalized medicine [1]. © 2006 IEEE.

引用

页码：14 / 18

页数：5

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