Identifi cation of diagnostic markers for tuberculosis by proteomic fi ngerprinting of serum

Agranoff, D; Fernandez-Reyes, D; Papadopoulos, M; Rojas, S; Herbster, M; Loosemore, A; Tarelli, E; Sheldon, J; Schwenk, A; Pollok, R; Rayner, C; Krishna, S

dc.contributor.author	Agranoff, D	en
dc.contributor.author	Fernandez-Reyes, D	en
dc.contributor.author	Papadopoulos, M	en
dc.contributor.author	Rojas, S	en
dc.contributor.author	Herbster, M	en
dc.contributor.author	Loosemore, A	en
dc.contributor.author	Tarelli, E	en
dc.contributor.author	Sheldon, J	en
dc.contributor.author	Schwenk, A	en
dc.contributor.author	Pollok, R	en
dc.contributor.author	Rayner, C	en
dc.contributor.author	Krishna, S	en
dc.date.accessioned	2014-03-01T01:55:03Z
dc.date.available	2014-03-01T01:55:03Z
dc.date.issued	2006	en
dc.identifier.uri	https://dspace.lib.ntua.gr/xmlui/handle/123456789/27574
dc.relation.uri	http://www.cs.ucl.ac.uk/staff/S.Rojas/index_files/2006_Lancet.pdf	en
dc.subject	C Reactive Protein	en
dc.subject	Clinical Feature	en
dc.subject	Correlation Analysis	en
dc.subject	Diagnostic Accuracy	en
dc.subject	Diagnostic Test	en
dc.subject	k-fold cross validation	en
dc.subject	Mass Spectrometry	en
dc.subject	Pattern Recognition	en
dc.subject	Random Sampling	en
dc.subject	Supervised Machine Learning	en
dc.subject	Support Vector Machine	en
dc.subject	Tuberculosis	en
dc.subject	Mass Spectrometric	en
dc.subject	serum amyloid a protein	en
dc.title	Identifi cation of diagnostic markers for tuberculosis by proteomic fi ngerprinting of serum	en
heal.type	journalArticle	en
heal.publicationDate	2006	en
heal.abstract	Summary Background We investigated the potential of proteomic fi ngerprinting with mass spectrometric serum profi ling, coupled with pattern recognition methods, to identify biomarkers that could improve diagnosis of tuberculosis. Methods We obtained serum proteomic profi les from patients with active tuberculosis and controls by surface-enhanced laser desorption ionisation time of fl ight mass spectrometry. A supervised machine-learning approach based	en