FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4366239108> ?p ?o ?g. }

• W4366239108 endingPage "e60" @default.
• W4366239108 startingPage "e60" @default.
• W4366239108 abstract "Abstract Unveiling the nucleic acid binding sites of a protein helps reveal its regulatory functions in vivo. Current methods encode protein sites from the handcrafted features of their local neighbors and recognize them via a classification, which are limited in expressive ability. Here, we present GeoBind, a geometric deep learning method for predicting nucleic binding sites on protein surface in a segmentation manner. GeoBind takes the whole point clouds of protein surface as input and learns the high-level representation based on the aggregation of their neighbors in local reference frames. Testing GeoBind on benchmark datasets, we demonstrate GeoBind is superior to state-of-the-art predictors. Specific case studies are performed to show the powerful ability of GeoBind to explore molecular surfaces when deciphering proteins with multimer formation. To show the versatility of GeoBind, we further extend GeoBind to five other types of ligand binding sites prediction tasks and achieve competitive performances." @default.
• W4366239108 created "2023-04-20" @default.
• W4366239108 creator A5001552700 @default.
• W4366239108 creator A5008775479 @default.
• W4366239108 date "2023-04-18" @default.
• W4366239108 modified "2023-09-30" @default.
• W4366239108 title "GeoBind: segmentation of nucleic acid binding interface on protein surface with geometric deep learning" @default.
• W4366239108 cites W1019830208 @default.
• W4366239108 cites W1596936080 @default.
• W4366239108 cites W1627614749 @default.
• W4366239108 cites W1842452185 @default.
• W4366239108 cites W1971894358 @default.
• W4366239108 cites W1976526581 @default.
• W4366239108 cites W1997535872 @default.
• W4366239108 cites W2018661561 @default.
• W4366239108 cites W2043638247 @default.
• W4366239108 cites W2046986600 @default.
• W4366239108 cites W2051210555 @default.
• W4366239108 cites W2065335843 @default.
• W4366239108 cites W2087064409 @default.
• W4366239108 cites W2100990361 @default.
• W4366239108 cites W2105525514 @default.
• W4366239108 cites W2108508060 @default.
• W4366239108 cites W2114850508 @default.
• W4366239108 cites W2115339329 @default.
• W4366239108 cites W2130479394 @default.
• W4366239108 cites W2140538197 @default.
• W4366239108 cites W2167884443 @default.
• W4366239108 cites W2168055922 @default.
• W4366239108 cites W2170747616 @default.
• W4366239108 cites W2171193618 @default.
• W4366239108 cites W2472103865 @default.
• W4366239108 cites W2558748708 @default.
• W4366239108 cites W2582176104 @default.
• W4366239108 cites W2785269871 @default.
• W4366239108 cites W2785792383 @default.
• W4366239108 cites W2900437486 @default.
• W4366239108 cites W2982552606 @default.
• W4366239108 cites W2992752586 @default.
• W4366239108 cites W3007077815 @default.
• W4366239108 cites W3014677172 @default.
• W4366239108 cites W3099878876 @default.
• W4366239108 cites W3126876258 @default.
• W4366239108 cites W3146944767 @default.
• W4366239108 cites W3177828909 @default.
• W4366239108 cites W3186179742 @default.
• W4366239108 cites W3216616254 @default.
• W4366239108 cites W4205773061 @default.
• W4366239108 cites W4210262283 @default.
• W4366239108 cites W4229031603 @default.
• W4366239108 cites W4233334497 @default.
• W4366239108 cites W4248318091 @default.
• W4366239108 cites W4293003482 @default.
• W4366239108 cites W4318071656 @default.
• W4366239108 doi "https://doi.org/10.1093/nar/gkad288" @default.
• W4366239108 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37070217" @default.
• W4366239108 hasPublicationYear "2023" @default.
• W4366239108 type Work @default.
• W4366239108 citedByCount "0" @default.
• W4366239108 crossrefType "journal-article" @default.
• W4366239108 hasAuthorship W4366239108A5001552700 @default.
• W4366239108 hasAuthorship W4366239108A5008775479 @default.
• W4366239108 hasBestOaLocation W43662391081 @default.
• W4366239108 hasConcept C104317684 @default.
• W4366239108 hasConcept C107824862 @default.
• W4366239108 hasConcept C13280743 @default.
• W4366239108 hasConcept C153180895 @default.
• W4366239108 hasConcept C154945302 @default.
• W4366239108 hasConcept C17744445 @default.
• W4366239108 hasConcept C185798385 @default.
• W4366239108 hasConcept C186060115 @default.
• W4366239108 hasConcept C199539241 @default.
• W4366239108 hasConcept C205649164 @default.
• W4366239108 hasConcept C24107716 @default.
• W4366239108 hasConcept C2524010 @default.
• W4366239108 hasConcept C2776359362 @default.
• W4366239108 hasConcept C2776799497 @default.
• W4366239108 hasConcept C28719098 @default.
• W4366239108 hasConcept C33923547 @default.
• W4366239108 hasConcept C41008148 @default.
• W4366239108 hasConcept C54355233 @default.
• W4366239108 hasConcept C66746571 @default.
• W4366239108 hasConcept C70721500 @default.
• W4366239108 hasConcept C86803240 @default.
• W4366239108 hasConcept C89600930 @default.
• W4366239108 hasConcept C94625758 @default.
• W4366239108 hasConceptScore W4366239108C104317684 @default.
• W4366239108 hasConceptScore W4366239108C107824862 @default.
• W4366239108 hasConceptScore W4366239108C13280743 @default.
• W4366239108 hasConceptScore W4366239108C153180895 @default.
• W4366239108 hasConceptScore W4366239108C154945302 @default.
• W4366239108 hasConceptScore W4366239108C17744445 @default.
• W4366239108 hasConceptScore W4366239108C185798385 @default.
• W4366239108 hasConceptScore W4366239108C186060115 @default.
• W4366239108 hasConceptScore W4366239108C199539241 @default.
• W4366239108 hasConceptScore W4366239108C205649164 @default.
• W4366239108 hasConceptScore W4366239108C24107716 @default.
• W4366239108 hasConceptScore W4366239108C2524010 @default.

• next