FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2762292317> ?p ?o ?g. }

• W2762292317 endingPage "111" @default.
• W2762292317 startingPage "103" @default.
• W2762292317 abstract "Proteomics data analysis based on the mass-spectrometry technique can provide a powerful tool for early diagnosis of tumors and other diseases. It can be used for exploring the features that reflect the difference between samples from high-throughput mass spectrometry data, which are important for the identification of tumor markers. Proteomics mass spectrometry data have the characteristics of too few samples, too many features and noise interference, which pose a great challenge to traditional machine learning methods. Traditional unsupervised dimensionality reduction methods do not utilize the label information effectively, so the subspaces they find may not be the most separable ones of the data. To overcome the shortcomings of traditional methods, in this paper, we present a novel feature selection method based on support vector machine (SVM) and shape analysis. In the process of feature selection, our method considers not only the interaction between features but also the relationship between features and class labels, which improves the classification performance. The experimental results obtained from four groups of proteomics data show that, compared with traditional unsupervised feature extraction methods (i.e., Principal Component Analysis - Procrustes Analysis, PCA-PA), our method not only ensures that fewer features are selected but also ensures a high recognition rate. In addition, compared with the two kinds of multivariate filter methods, i.e., Max-Relevance Min-Redundancy (MRMR) and Fast Correlation-Based Filter (FCBF), our method has a higher recognition rate." @default.
• W2762292317 created "2017-10-20" @default.
• W2762292317 creator A5012301761 @default.
• W2762292317 creator A5066614283 @default.
• W2762292317 creator A5070005587 @default.
• W2762292317 date "2017-12-01" @default.
• W2762292317 modified "2023-10-15" @default.
• W2762292317 title "Feature selection method based on support vector machine and shape analysis for high-throughput medical data" @default.
• W2762292317 cites W1538496608 @default.
• W2762292317 cites W1548832181 @default.
• W2762292317 cites W1580817707 @default.
• W2762292317 cites W1581837830 @default.
• W2762292317 cites W1606191370 @default.
• W2762292317 cites W1703245085 @default.
• W2762292317 cites W1807984730 @default.
• W2762292317 cites W1952030594 @default.
• W2762292317 cites W1980100242 @default.
• W2762292317 cites W1985560419 @default.
• W2762292317 cites W1987919734 @default.
• W2762292317 cites W1992006167 @default.
• W2762292317 cites W2003319928 @default.
• W2762292317 cites W2013396429 @default.
• W2762292317 cites W2015550989 @default.
• W2762292317 cites W2019349647 @default.
• W2762292317 cites W2034279806 @default.
• W2762292317 cites W2054346287 @default.
• W2762292317 cites W2092461479 @default.
• W2762292317 cites W2094221068 @default.
• W2762292317 cites W2095077236 @default.
• W2762292317 cites W2100658396 @default.
• W2762292317 cites W2103266366 @default.
• W2762292317 cites W2119314811 @default.
• W2762292317 cites W2122606486 @default.
• W2762292317 cites W2132549764 @default.
• W2762292317 cites W2154053567 @default.
• W2762292317 cites W2162833766 @default.
• W2762292317 cites W2163443023 @default.
• W2762292317 cites W2165580920 @default.
• W2762292317 cites W2165885026 @default.
• W2762292317 cites W2169268001 @default.
• W2762292317 cites W2522099692 @default.
• W2762292317 cites W2560241536 @default.
• W2762292317 cites W2570205435 @default.
• W2762292317 cites W2570565373 @default.
• W2762292317 cites W2585498886 @default.
• W2762292317 cites W2589064605 @default.
• W2762292317 cites W2589648225 @default.
• W2762292317 cites W2590160933 @default.
• W2762292317 cites W2593693290 @default.
• W2762292317 cites W2596198811 @default.
• W2762292317 cites W3103053050 @default.
• W2762292317 doi "https://doi.org/10.1016/j.compbiomed.2017.10.008" @default.
• W2762292317 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/29049908" @default.
• W2762292317 hasPublicationYear "2017" @default.
• W2762292317 type Work @default.
• W2762292317 sameAs 2762292317 @default.
• W2762292317 citedByCount "30" @default.
• W2762292317 countsByYear W27622923172018 @default.
• W2762292317 countsByYear W27622923172019 @default.
• W2762292317 countsByYear W27622923172020 @default.
• W2762292317 countsByYear W27622923172021 @default.
• W2762292317 countsByYear W27622923172022 @default.
• W2762292317 countsByYear W27622923172023 @default.
• W2762292317 crossrefType "journal-article" @default.
• W2762292317 hasAuthorship W2762292317A5012301761 @default.
• W2762292317 hasAuthorship W2762292317A5066614283 @default.
• W2762292317 hasAuthorship W2762292317A5070005587 @default.
• W2762292317 hasConcept C111919701 @default.
• W2762292317 hasConcept C119857082 @default.
• W2762292317 hasConcept C12267149 @default.
• W2762292317 hasConcept C124101348 @default.
• W2762292317 hasConcept C148483581 @default.
• W2762292317 hasConcept C152124472 @default.
• W2762292317 hasConcept C153180895 @default.
• W2762292317 hasConcept C154945302 @default.
• W2762292317 hasConcept C27438332 @default.
• W2762292317 hasConcept C41008148 @default.
• W2762292317 hasConcept C52622490 @default.
• W2762292317 hasConcept C70518039 @default.
• W2762292317 hasConceptScore W2762292317C111919701 @default.
• W2762292317 hasConceptScore W2762292317C119857082 @default.
• W2762292317 hasConceptScore W2762292317C12267149 @default.
• W2762292317 hasConceptScore W2762292317C124101348 @default.
• W2762292317 hasConceptScore W2762292317C148483581 @default.
• W2762292317 hasConceptScore W2762292317C152124472 @default.
• W2762292317 hasConceptScore W2762292317C153180895 @default.
• W2762292317 hasConceptScore W2762292317C154945302 @default.
• W2762292317 hasConceptScore W2762292317C27438332 @default.
• W2762292317 hasConceptScore W2762292317C41008148 @default.
• W2762292317 hasConceptScore W2762292317C52622490 @default.
• W2762292317 hasConceptScore W2762292317C70518039 @default.
• W2762292317 hasFunder F4320321001 @default.
• W2762292317 hasFunder F4320322186 @default.
• W2762292317 hasFunder F4320334974 @default.
• W2762292317 hasLocation W27622923171 @default.
• W2762292317 hasLocation W27622923172 @default.
• W2762292317 hasOpenAccess W2762292317 @default.
• W2762292317 hasPrimaryLocation W27622923171 @default.

• next