FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W3129322014> ?p ?o ?g. }

• W3129322014 endingPage "2339" @default.
• W3129322014 startingPage "2332" @default.
• W3129322014 abstract "Effective use of evolutionary information has recently led to tremendous progress in computational prediction of three-dimensional (3D) structures of proteins and their complexes. Despite the progress, the accuracy of predicted structures tends to vary considerably from case to case. Since the utility of computational models depends on their accuracy, reliable estimates of deviation between predicted and native structures are of utmost importance.For the first time, we present a deep convolutional neural network (CNN) constructed on a Voronoi tessellation of 3D molecular structures. Despite the irregular data domain, our data representation allows us to efficiently introduce both convolution and pooling operations and train the network in an end-to-end fashion without precomputed descriptors. The resultant model, VoroCNN, predicts local qualities of 3D protein folds. The prediction results are competitive to state of the art and superior to the previous 3D CNN architectures built for the same task. We also discuss practical applications of VoroCNN, for example, in recognition of protein binding interfaces.The model, data and evaluation tests are available at https://team.inria.fr/nano-d/software/vorocnn/.Supplementary data are available at Bioinformatics online." @default.
• W3129322014 created "2021-03-01" @default.
• W3129322014 creator A5009307435 @default.
• W3129322014 creator A5031938940 @default.
• W3129322014 creator A5041591967 @default.
• W3129322014 creator A5067914405 @default.
• W3129322014 creator A5079599346 @default.
• W3129322014 date "2021-02-23" @default.
• W3129322014 modified "2023-10-04" @default.
• W3129322014 title "VoroCNN: deep convolutional neural network built on 3D Voronoi tessellation of protein structures" @default.
• W3129322014 cites W1554093359 @default.
• W3129322014 cites W2017285544 @default.
• W3129322014 cites W2062351773 @default.
• W3129322014 cites W2081290596 @default.
• W3129322014 cites W2081897966 @default.
• W3129322014 cites W2113267464 @default.
• W3129322014 cites W2113623846 @default.
• W3129322014 cites W2116341502 @default.
• W3129322014 cites W2120796076 @default.
• W3129322014 cites W2135957668 @default.
• W3129322014 cites W2140673705 @default.
• W3129322014 cites W2144046885 @default.
• W3129322014 cites W2177731497 @default.
• W3129322014 cites W2287165934 @default.
• W3129322014 cites W2508457857 @default.
• W3129322014 cites W2534288757 @default.
• W3129322014 cites W2558748708 @default.
• W3129322014 cites W2594725344 @default.
• W3129322014 cites W2604474471 @default.
• W3129322014 cites W2606314633 @default.
• W3129322014 cites W2770647690 @default.
• W3129322014 cites W2784883284 @default.
• W3129322014 cites W2801109052 @default.
• W3129322014 cites W2889674656 @default.
• W3129322014 cites W2905812122 @default.
• W3129322014 cites W2943867488 @default.
• W3129322014 cites W2949382809 @default.
• W3129322014 cites W2950538451 @default.
• W3129322014 cites W2951621517 @default.
• W3129322014 cites W2951639995 @default.
• W3129322014 cites W2951690294 @default.
• W3129322014 cites W2953524386 @default.
• W3129322014 cites W2962831078 @default.
• W3129322014 cites W2963121313 @default.
• W3129322014 cites W2966057938 @default.
• W3129322014 cites W2967606876 @default.
• W3129322014 cites W2969735716 @default.
• W3129322014 cites W2971003495 @default.
• W3129322014 cites W2971733377 @default.
• W3129322014 cites W2980272550 @default.
• W3129322014 cites W2999044305 @default.
• W3129322014 cites W3105297345 @default.
• W3129322014 cites W3199799076 @default.
• W3129322014 cites W4210257598 @default.
• W3129322014 cites W4241935390 @default.
• W3129322014 doi "https://doi.org/10.1093/bioinformatics/btab118" @default.
• W3129322014 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33620450" @default.
• W3129322014 hasPublicationYear "2021" @default.
• W3129322014 type Work @default.
• W3129322014 sameAs 3129322014 @default.
• W3129322014 citedByCount "24" @default.
• W3129322014 countsByYear W31293220142020 @default.
• W3129322014 countsByYear W31293220142021 @default.
• W3129322014 countsByYear W31293220142022 @default.
• W3129322014 countsByYear W31293220142023 @default.
• W3129322014 crossrefType "journal-article" @default.
• W3129322014 hasAuthorship W3129322014A5009307435 @default.
• W3129322014 hasAuthorship W3129322014A5031938940 @default.
• W3129322014 hasAuthorship W3129322014A5041591967 @default.
• W3129322014 hasAuthorship W3129322014A5067914405 @default.
• W3129322014 hasAuthorship W3129322014A5079599346 @default.
• W3129322014 hasBestOaLocation W31293220142 @default.
• W3129322014 hasConcept C108583219 @default.
• W3129322014 hasConcept C119857082 @default.
• W3129322014 hasConcept C121684516 @default.
• W3129322014 hasConcept C124101348 @default.
• W3129322014 hasConcept C134306372 @default.
• W3129322014 hasConcept C153180895 @default.
• W3129322014 hasConcept C154945302 @default.
• W3129322014 hasConcept C17744445 @default.
• W3129322014 hasConcept C199360897 @default.
• W3129322014 hasConcept C199539241 @default.
• W3129322014 hasConcept C24881265 @default.
• W3129322014 hasConcept C2524010 @default.
• W3129322014 hasConcept C2776359362 @default.
• W3129322014 hasConcept C2777904410 @default.
• W3129322014 hasConcept C33923547 @default.
• W3129322014 hasConcept C36503486 @default.
• W3129322014 hasConcept C41008148 @default.
• W3129322014 hasConcept C43817857 @default.
• W3129322014 hasConcept C45347329 @default.
• W3129322014 hasConcept C50644808 @default.
• W3129322014 hasConcept C70437156 @default.
• W3129322014 hasConcept C81363708 @default.
• W3129322014 hasConcept C94625758 @default.
• W3129322014 hasConceptScore W3129322014C108583219 @default.
• W3129322014 hasConceptScore W3129322014C119857082 @default.
• W3129322014 hasConceptScore W3129322014C121684516 @default.

• next