Matches in SemOpenAlex for { <https://semopenalex.org/work/W3090656100> ?p ?o ?g. }
Showing items 1 to 73 of
73
with 100 items per page.
- W3090656100 abstract "Prediction of high grade ovarian cancer on proteomic data is a clinical challenge. Besides, it offers the potential for earlier intervention to increase overall survival, as well as guides the prophylactic ovarian removal to avoid unnecessary early menopause. In this work, we propose a model that learns how to detect ovarian cancer on images from uterine liquid proteomic data. The contributions of this work are two-fold. First, we propose an original method to use proteomic data without direct matching with the existing protein libraries as in the traditional method. The gray-scale peptide image generated by our method contains almost all information from mass spectrometry. Second, we pioneer in analyzing the uterine liquid proteomic data with deep convolutional neural networks. Specifically, we design a feature extractor consisting of stacked asymmetric convolutional layers, which could pay more attention to multiple compounds in different retention times and isotopes in similar mass/charge than symmetric convolutions. Another novelty is trying to find the patches contributing more in improving both sensitivity and specificity. In addition, we add an auxiliary classifier module near the end of training to push useful gradients into the lower layers and to improve the convergence during training. Compared with traditional proteome analysis, experimental results demonstrate the effectiveness and superiority of our model in high grade ovarian cancer prediction." @default.
- W3090656100 created "2020-10-08" @default.
- W3090656100 creator A5034064562 @default.
- W3090656100 creator A5079242743 @default.
- W3090656100 creator A5081179416 @default.
- W3090656100 date "2020-01-01" @default.
- W3090656100 modified "2023-09-25" @default.
- W3090656100 title "Ovarian Cancer Prediction in Proteomic Data Using Stacked Asymmetric Convolution" @default.
- W3090656100 cites W1543068333 @default.
- W3090656100 cites W1949412677 @default.
- W3090656100 cites W2054555809 @default.
- W3090656100 cites W2075711878 @default.
- W3090656100 cites W2097308130 @default.
- W3090656100 cites W2214802907 @default.
- W3090656100 cites W2291806982 @default.
- W3090656100 cites W2594216872 @default.
- W3090656100 cites W2913168067 @default.
- W3090656100 doi "https://doi.org/10.1007/978-3-030-59713-9_26" @default.
- W3090656100 hasPublicationYear "2020" @default.
- W3090656100 type Work @default.
- W3090656100 sameAs 3090656100 @default.
- W3090656100 citedByCount "0" @default.
- W3090656100 crossrefType "book-chapter" @default.
- W3090656100 hasAuthorship W3090656100A5034064562 @default.
- W3090656100 hasAuthorship W3090656100A5079242743 @default.
- W3090656100 hasAuthorship W3090656100A5081179416 @default.
- W3090656100 hasConcept C104397665 @default.
- W3090656100 hasConcept C117978034 @default.
- W3090656100 hasConcept C121608353 @default.
- W3090656100 hasConcept C126322002 @default.
- W3090656100 hasConcept C127413603 @default.
- W3090656100 hasConcept C153180895 @default.
- W3090656100 hasConcept C154945302 @default.
- W3090656100 hasConcept C21880701 @default.
- W3090656100 hasConcept C2780427987 @default.
- W3090656100 hasConcept C41008148 @default.
- W3090656100 hasConcept C60644358 @default.
- W3090656100 hasConcept C71924100 @default.
- W3090656100 hasConcept C81363708 @default.
- W3090656100 hasConcept C86803240 @default.
- W3090656100 hasConcept C95623464 @default.
- W3090656100 hasConceptScore W3090656100C104397665 @default.
- W3090656100 hasConceptScore W3090656100C117978034 @default.
- W3090656100 hasConceptScore W3090656100C121608353 @default.
- W3090656100 hasConceptScore W3090656100C126322002 @default.
- W3090656100 hasConceptScore W3090656100C127413603 @default.
- W3090656100 hasConceptScore W3090656100C153180895 @default.
- W3090656100 hasConceptScore W3090656100C154945302 @default.
- W3090656100 hasConceptScore W3090656100C21880701 @default.
- W3090656100 hasConceptScore W3090656100C2780427987 @default.
- W3090656100 hasConceptScore W3090656100C41008148 @default.
- W3090656100 hasConceptScore W3090656100C60644358 @default.
- W3090656100 hasConceptScore W3090656100C71924100 @default.
- W3090656100 hasConceptScore W3090656100C81363708 @default.
- W3090656100 hasConceptScore W3090656100C86803240 @default.
- W3090656100 hasConceptScore W3090656100C95623464 @default.
- W3090656100 hasLocation W30906561001 @default.
- W3090656100 hasOpenAccess W3090656100 @default.
- W3090656100 hasPrimaryLocation W30906561001 @default.
- W3090656100 hasRelatedWork W2582698 @default.
- W3090656100 hasRelatedWork W2834797 @default.
- W3090656100 hasRelatedWork W4947539 @default.
- W3090656100 hasRelatedWork W5568854 @default.
- W3090656100 hasRelatedWork W6494939 @default.
- W3090656100 hasRelatedWork W6680660 @default.
- W3090656100 hasRelatedWork W7626849 @default.
- W3090656100 hasRelatedWork W844961 @default.
- W3090656100 hasRelatedWork W9190101 @default.
- W3090656100 hasRelatedWork W9641522 @default.
- W3090656100 isParatext "false" @default.
- W3090656100 isRetracted "false" @default.
- W3090656100 magId "3090656100" @default.
- W3090656100 workType "book-chapter" @default.