Machine learning in cancer research: Implications for personalised medicine

Alfredo Vellido; Elia Biganzoli; Paulo J G Lisboa

Machine learning in cancer research: Implications for personalised medicine

Alfredo Vellido, Elia Biganzoli, Paulo J G Lisboa

Fondazione IRCCS Istituto Nazionale dei Tumori

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis.

Original language	English
Title of host publication	ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning
Pages	55-64
Number of pages	10
Publication status	Published - 2008
Event	16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008 - Bruges, Belgium Duration: Apr 23 2008 → Apr 25 2008

Other

Other	16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008
Country/Territory	Belgium
City	Bruges
Period	4/23/08 → 4/25/08

ASJC Scopus subject areas

Artificial Intelligence
Information Systems

Cite this

Machine learning in cancer research : Implications for personalised medicine. / Vellido, Alfredo; Biganzoli, Elia; Lisboa, Paulo J G.

ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. 2008. p. 55-64.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Vellido, A, Biganzoli, E & Lisboa, PJG 2008, Machine learning in cancer research: Implications for personalised medicine. in ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning. pp. 55-64, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008, Bruges, Belgium, 4/23/08.

@inproceedings{dac6b7067c8846b79ef0b43455cb2853,

title = "Machine learning in cancer research: Implications for personalised medicine",

abstract = "Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis.",

author = "Alfredo Vellido and Elia Biganzoli and Lisboa, {Paulo J G}",

year = "2008",

language = "English",

isbn = "2930307080",

pages = "55--64",

booktitle = "ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning",

note = "16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008 ; Conference date: 23-04-2008 Through 25-04-2008",

}

TY - GEN

T1 - Machine learning in cancer research

T2 - 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning, ESANN 2008

AU - Vellido, Alfredo

AU - Biganzoli, Elia

AU - Lisboa, Paulo J G

PY - 2008

Y1 - 2008

N2 - Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis.

AB - Driven by the growing demand of personalization of medical procedures, data-based, computer-aided cancer research in human patients is advancing at an accelerating pace, providing a broadening landscape of opportunity for Machine Learning methods. This landscape can be observed from the wide-reaching view of population studies down to the genotype detail. In this brief paper, we provide a sweeping glimpse, by no means exhaustive, of the state-of-the-art in this field at the different scales of data measurement and analysis.

UR - http://www.scopus.com/inward/record.url?scp=80054740157&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80054740157&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:80054740157

SN - 2930307080

SN - 9782930307084

SP - 55

EP - 64

BT - ESANN 2008 Proceedings, 16th European Symposium on Artificial Neural Networks - Advances in Computational Intelligence and Learning

Y2 - 23 April 2008 through 25 April 2008

ER -

Machine learning in cancer research: Implications for personalised medicine

Abstract

Other

ASJC Scopus subject areas

Other files and links

Fingerprint

Cite this