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]. This article is not intended as a comprehensive survey of data mining applications in cancer. Rather, it provides starting points for further, more targeted, literature searches, by embarking on a guided tour of computational intelligence applications in cancer medicine, structured in increasing order of the physical scales of biological processes, as outlined in Table 1.

Data Mining in Cancer Research

TAGLIAFERRI, Roberto;
2010

Abstract

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]. This article is not intended as a comprehensive survey of data mining applications in cancer. Rather, it provides starting points for further, more targeted, literature searches, by embarking on a guided tour of computational intelligence applications in cancer medicine, structured in increasing order of the physical scales of biological processes, as outlined in Table 1.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11386/2600428
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