Machine Science in Biomedicine: Practicalities, Pitfalls and Potential

Tom Kelsey; W H B Wallace

Machine Science in Biomedicine: Practicalities, Pitfalls and Potential

Tom Kelsey, W H B Wallace

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

Abstract

Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining, data assessment, costs, limitations, skills and tool support.

Original language	English
Title of host publication	Proceedings of the First Workshop on Knowledge Engineering, Discovery and Dissemination in Health (KEDDH10)
Subtitle of host publication	Proc. KEDDH10
Publisher	IEEE Computer Society
Pages	117-122
Number of pages	6
ISBN (Print)	978-1-4244-8302-0
Publication status	Published - 18 Dec 2010

Cite this

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title = "Machine Science in Biomedicine: Practicalities, Pitfalls and Potential",

abstract = "Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining, data assessment, costs, limitations, skills and tool support. ",

author = "Tom Kelsey and Wallace, {W H B}",

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T1 - Machine Science in Biomedicine: Practicalities, Pitfalls and Potential

AU - Kelsey, Tom

AU - Wallace, W H B

PY - 2010/12/18

Y1 - 2010/12/18

N2 - Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining, data assessment, costs, limitations, skills and tool support.

AB - Machine Science, or Data-driven Research, is a new and interesting scientific methodology that uses advanced computational techniques to identify, retrieve, classify and analyse data in order to generate hypotheses and develop models. In this paper we describe three recent biomedical Machine Science studies, and use these to assess the current state of the art with specific emphasis on data mining, data assessment, costs, limitations, skills and tool support.

M3 - Conference contribution

SN - 978-1-4244-8302-0

SP - 117

EP - 122

BT - Proceedings of the First Workshop on Knowledge Engineering, Discovery and Dissemination in Health (KEDDH10)

PB - IEEE Computer Society

ER -

Machine Science in Biomedicine: Practicalities, Pitfalls and Potential

Abstract

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Cite this