Train-spotting: building classifiers for microarrays

Yuxuan Lan; G. C. Cawley; R. W. Harvey

doi:10.1109/IJCNN.2003.1224037

Train-spotting: building classifiers for microarrays

Research output: Contribution to conference › Paper

Abstract

The problem of extracting spots from DNA microarrays is a problem of considerable scientific and economic utility. In this paper we introduce a new approach based on a scale-space analysis of the image. We augment this with a machine learning system that guides an operator by classifying spots into those that require further attention and those that are already segmented correctly. We compare conventional k-nearest neighbor techniques with generalized linear models and multilayer perceptrons using confidence intervals and McNemar's test.

Original language	English
Pages	2934-2939
Number of pages	6
DOIs	https://doi.org/10.1109/IJCNN.2003.1224037
Publication status	Published - 2003
Event	IEEE/INNS International Joint Conference on Artificial Neural Networks - Portland, United States Duration: 20 Jul 2003 → 24 Jul 2003

Conference

Conference	IEEE/INNS International Joint Conference on Artificial Neural Networks
Abbreviated title	IJCNN-2003
Country/Territory	United States
City	Portland
Period	20/07/03 → 24/07/03

Access to Document

10.1109/IJCNN.2003.1224037

Cite this

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title = "Train-spotting: building classifiers for microarrays",

abstract = "The problem of extracting spots from DNA microarrays is a problem of considerable scientific and economic utility. In this paper we introduce a new approach based on a scale-space analysis of the image. We augment this with a machine learning system that guides an operator by classifying spots into those that require further attention and those that are already segmented correctly. We compare conventional k-nearest neighbor techniques with generalized linear models and multilayer perceptrons using confidence intervals and McNemar's test.",

author = "Yuxuan Lan and Cawley, {G. C.} and Harvey, {R. W.}",

year = "2003",

doi = "10.1109/IJCNN.2003.1224037",

language = "English",

pages = "2934--2939",

note = "IEEE/INNS International Joint Conference on Artificial Neural Networks, IJCNN-2003 ; Conference date: 20-07-2003 Through 24-07-2003",

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AU - Cawley, G. C.

AU - Harvey, R. W.

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AB - The problem of extracting spots from DNA microarrays is a problem of considerable scientific and economic utility. In this paper we introduce a new approach based on a scale-space analysis of the image. We augment this with a machine learning system that guides an operator by classifying spots into those that require further attention and those that are already segmented correctly. We compare conventional k-nearest neighbor techniques with generalized linear models and multilayer perceptrons using confidence intervals and McNemar's test.

U2 - 10.1109/IJCNN.2003.1224037

DO - 10.1109/IJCNN.2003.1224037

M3 - Paper

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T2 - IEEE/INNS International Joint Conference on Artificial Neural Networks

Y2 - 20 July 2003 through 24 July 2003

ER -