A Rule-Based Scheme for Filtering Examples from Majority Class in an Imbalanced Training Set

Dehmeshki, Jamshid; Karakoy, Mustafa; Casique, M.V.

doi:10.1007/3-540-45065-3_19

Cited by 7 publications

(1 citation statement)

References 2 publications

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“…The first TS version obtained from SET1, for example, contained 40184 peaks of the class "background" and 1022 peaks of the class "golden". Such an imbalanced TS often causes machine learning algorithms to perform poorly on the minority class, i.e., the rare instances are often treated as noise (Dehmeshki et al, 2003). In order to make both classes have the same weight for the model construction, the TS is balanced using a supervised instance resampling algorithm.…”

Section: Balancing Of Tsmentioning

confidence: 99%

Improving Phosphopeptide/Protein Identification Using a New Data Mining Framework for MS/MS Spectra Preprocessing

Cerqueira¹,

Mor²,

Ascher³

et al. 2009

J Proteomics Bioinform

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Phosphopeptide/protein identification using tandem mass spectrometry (MS/MS) is a challenging issue in proteomics research. In particular, phosphopeptides typically exhibit low intensity peaks of b and y ions in spectra when serine or threonine is phosphorylated. Consequently, the existing algorithms for peptide and protein identification generate a high false discovery rate when coping with phosphopeptide spectra. In order to increase the number of correct phosphopeptide identifications using database search, a new data mining approach for spectra preprocessing is proposed. A support vector machine classifier is used to calculate the probability of each peak representing a b or y ion. Next, low-probability peaks are removed from spectra, while remaining peaks have their intensities enhanced. As a result, a huge increase in signal-to-noise ratio is provided and the chances of detecting important peaks are significantly advanced. Experiments using MASCOT and SEQUEST along with Peptide/ProteinProphet and a decoy database approach showed a significant improvement in the sensitivity of phosphopeptide identification without compromising specificity, demonstrating that our new strategy for MS/MS spectra preprocessing is a powerful proteomics tool for enhancing phosphopeptide identifications.

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Section: Balancing Of Tsmentioning

confidence: 99%