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

Volumn , Issue , 2012, Pages 423-428

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

^a UNIVERSITY OF GRONINGEN   (Netherlands)

^b UNIVERSITY OF BIRMINGHAM   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

ARTIFICIAL INTELLIGENCE; DIAGNOSIS; NEURAL NETWORKS; TUMORS; VECTOR QUANTIZATION;

DIAGNOSTIC TOOLS; DIFFERENTIAL DIAGNOSIS; GENERALIZED LEARNING VECTOR QUANTIZATION; HIGH SENSITIVITY; HIGH THROUGHPUT; PROTOTYPE-BASED CLASSIFICATIONS; RELEVANCE LEARNING; URINARY EXCRETION;

LEARNING SYSTEMS;

EID: 84947792616     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (10)

1. The European Network for the Study of Adrenal Tumours (ENS@T), url: www.ensat.org
2. W. Arlt, M. Biehl, A.E. Taylor, S. Hahner, R. Libé, B.A. Hughes, P. Schneider, D.J. Smith, H. Stiekema, N. Krone, E. Porfiri, G. Opocher, J. Bertherat, F. Mantero, B. Allolio, M. Terzolo, P. Nightingale, C.H.L. Shackleton, X. Bertagna, M. Fassnacht, and P.M. Stewart, Urine steroid metabolomics as a biomarker tool for detecting malignancy in adrenal tumors, J. Clinical Endocrinology & Metabolism, 96: 3775-3784, 2011.
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