An intensity-region driven multi-classifier scheme for improving the classification accuracy of proteomic MS-spectra

Computer Methods and Programs in Biomedicine

Volumn 99, Issue 2, 2010, Pages 147-153

  Bougioukos, Panagiotis ^a   Glotsos, Dimitris ^b   Cavouras, Dionisis ^b   Daskalakis, Antonis ^a   Kalatzis, Ioannis ^b   Kostopoulos, Spiros ^a   Nikiforidis, George ^a   Bezerianos, Anastasios ^a  

^a UNIVERSITY OF PATRAS   (Greece)

^b UNIVERSITY OF WEST ATTICA   (Greece)

Author keywords

Classification; Ovarian cancer; Pre processing

Indexed keywords

CLASSIFICATION ACCURACY; CROSS VALIDATION; DATA SETS; K-NEAREST NEIGHBOURS; MAJORITY VOTE; MEAN SENSITIVITY; MULTI-CLASSIFIER; OVARIAN CANCERS; PRE-PROCESSING; PROBABILISTIC NEURAL NETWORKS; RELIABLE ESTIMATES; Z VALUE;

CLASSIFIERS; NEURAL NETWORKS;

PATTERN RECOGNITION SYSTEMS;

BIOLOGICAL MARKER;

ARTICLE; CANCER CLASSIFICATION; CLASSIFICATION ALGORITHM; CONTROLLED STUDY; MASS SPECTROMETRY; OVARY CANCER; PATTERN RECOGNITION; PERFORMANCE; PROTEOMICS; SENSITIVITY AND SPECIFICITY; SUPPORT VECTOR MACHINE; VALIDATION PROCESS; ARTIFICIAL NEURAL NETWORK; FACTUAL DATABASE; FEMALE; HUMAN; METABOLISM; OVARY TUMOR; PROCEDURES;

BIOLOGICAL MARKERS; DATABASES, FACTUAL; FEMALE; HUMANS; MASS SPECTROMETRY; NEURAL NETWORKS (COMPUTER); OVARIAN NEOPLASMS; PROTEOMICS;

EID: 77954315481     PISSN: 01692607     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.cmpb.2009.11.003     Document Type: Article

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