Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

M. Biehl; P. Schneider; D. J. Smith; H. Stiekema; A. E. Taylor; B. A. Hughes; C. H.L. Shackleton; P. M. Stewart; W. Arlt

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

M. Biehl, P. Schneider, D. J. Smith, H. Stiekema, A. E. Taylor, B. A. Hughes, C. H.L. Shackleton, P. M. Stewart, W. Arlt

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

27 Citations (Scopus)

Abstract

We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototype-based classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the detection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a noninvasive high-throughput diagnostic tool.

Original language	English
Title of host publication	ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning
Publisher	i6doc.com publication
Pages	423-428
Number of pages	6
ISBN (Print)	9782874190490
Publication status	Published - 2012
Event	20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 - Bruges, Belgium Duration: 25 Apr 2012 → 27 Apr 2012

Publication series

Name	ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

Conference

Conference	20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012
Country/Territory	Belgium
City	Bruges
Period	25/04/12 → 27/04/12

Bibliographical note

Publisher Copyright:
© 2012, i6doc.com publication. All rights reserved.

ASJC Scopus subject areas

Information Systems
Artificial Intelligence

Cite this

Biehl, M., Schneider, P., Smith, D. J., Stiekema, H., Taylor, A. E., Hughes, B. A., Shackleton, C. H. L., Stewart, P. M., & Arlt, W. (2012). Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. In ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (pp. 423-428). (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning). i6doc.com publication.

Biehl, M. ; Schneider, P. ; Smith, D. J. et al. / Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2012. pp. 423-428 (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

@inproceedings{3bbb37457b9c433799173092ac888498,

title = "Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors",

abstract = "We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototype-based classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the detection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a noninvasive high-throughput diagnostic tool.",

author = "M. Biehl and P. Schneider and Smith, {D. J.} and H. Stiekema and Taylor, {A. E.} and Hughes, {B. A.} and Shackleton, {C. H.L.} and Stewart, {P. M.} and W. Arlt",

note = "Publisher Copyright: {\textcopyright} 2012, i6doc.com publication. All rights reserved.; 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012 ; Conference date: 25-04-2012 Through 27-04-2012",

year = "2012",

language = "English",

isbn = "9782874190490",

series = "ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

publisher = "i6doc.com publication",

pages = "423--428",

booktitle = "ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning",

}

Biehl, M, Schneider, P, Smith, DJ, Stiekema, H, Taylor, AE, Hughes, BA, Shackleton, CHL, Stewart, PM & Arlt, W 2012, Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. in ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, i6doc.com publication, pp. 423-428, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012, Bruges, Belgium, 25/04/12.

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. / Biehl, M.; Schneider, P.; Smith, D. J. et al.
ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication, 2012. p. 423-428 (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

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

TY - GEN

T1 - Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

AU - Biehl, M.

AU - Schneider, P.

AU - Smith, D. J.

AU - Stiekema, H.

AU - Taylor, A. E.

AU - Hughes, B. A.

AU - Shackleton, C. H.L.

AU - Stewart, P. M.

AU - Arlt, W.

PY - 2012

Y1 - 2012

N2 - We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototype-based classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the detection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a noninvasive high-throughput diagnostic tool.

AB - We present a machine learning system for the differential diagnosis of benign adrenocortical adenoma (ACA) vs. malignant adrenocortical carcinoma (ACC). The data employed for the classification are urinary excretion values of 32 steroid metabolites. We apply prototype-based classification techniques to discriminate the classes, in particular, we use modifications of Generalized Learning Vector Quantization including matrix relevance learning. The obtained system achieves high sensitivity and specificity and outperforms previously used approaches for the detection of adrenal malignancy. Moreover, the method identifies a subset of most discriminative markers which facilitates its future use as a noninvasive high-throughput diagnostic tool.

UR - http://www.scopus.com/inward/record.url?scp=84947792616&partnerID=8YFLogxK

M3 - Conference contribution

AN - SCOPUS:84947792616

SN - 9782874190490

T3 - ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

SP - 423

EP - 428

BT - ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning

PB - i6doc.com publication

T2 - 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, ESANN 2012

Y2 - 25 April 2012 through 27 April 2012

ER -

Biehl M, Schneider P, Smith DJ, Stiekema H, Taylor AE, Hughes BA et al. Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors. In ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. i6doc.com publication. 2012. p. 423-428. (ESANN 2012 proceedings, 20th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning).

Matrix relevance LVQ in steroid metabolomics based classification of adrenal tumors

Abstract

Publication series

Conference

Bibliographical note

ASJC Scopus subject areas

Fingerprint

Cite this