FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W2949275670> ?p ?o ?g. }

• W2949275670 abstract "Abstract Modeling in-vivo protein-DNA binding is not only fundamental for further understanding of the regulatory mechanisms, but also a challenging task in computational biology. Deep-learning based methods have succeed in modeling in-vivo protein-DNA binding, but they often (1) follow the fully supervised learning framework and overlook the weakly supervised information of genomic sequences that a bound DNA sequence may has multiple TFBS(s), and, (2) use one-hot encoding to encode DNA sequences and ignore the dependencies among nucleotides. In this paper, we propose a weakly supervised framework, which combines multiple-instance learning with a hybrid deep neural network and uses k -mer encoding to transform DNA sequences, for modeling in-vivo protein-DNA binding. Firstly, this framework segments sequences into multiple overlapping instances using a sliding window, and then encodes all instances into image-like inputs of high-order dependencies using k -mer encoding. Secondly, it separately computes a score for all instances in the same bag using a hybrid deep neural network that integrates convolutional and recurrent neural networks. Finally, it integrates the predicted values of all instances as the final prediction of this bag using the Noisy-and method. The experimental results on in-vivo datasets demonstrate the superior performance of the proposed framework. In addition, we also explore the performance of the proposed framework when using k -mer encoding, and demonstrate the performance of the Noisy-and method by comparing it with other fusion methods, and find that adding recurrent layers can improve the performance of the proposed framework." @default.
• W2949275670 created "2019-06-27" @default.
• W2949275670 creator A5062540383 @default.
• W2949275670 creator A5089051425 @default.
• W2949275670 date "2019-06-11" @default.
• W2949275670 modified "2023-10-18" @default.
• W2949275670 title "Modeling in-vivo protein-DNA binding by combining multiple-instance learning with a hybrid deep neural network" @default.
• W2949275670 cites W1019830208 @default.
• W2949275670 cites W1518026427 @default.
• W2949275670 cites W1768880427 @default.
• W2949275670 cites W1976526581 @default.
• W2949275670 cites W1987951943 @default.
• W2949275670 cites W1990893875 @default.
• W2949275670 cites W2001012564 @default.
• W2949275670 cites W2007618103 @default.
• W2949275670 cites W2015622114 @default.
• W2949275670 cites W2028928739 @default.
• W2949275670 cites W2034145535 @default.
• W2949275670 cites W2042392223 @default.
• W2949275670 cites W2064675550 @default.
• W2949275670 cites W2090123586 @default.
• W2949275670 cites W2092988184 @default.
• W2949275670 cites W2094397139 @default.
• W2949275670 cites W2104865962 @default.
• W2949275670 cites W2112717966 @default.
• W2949275670 cites W2118886215 @default.
• W2949275670 cites W2127616922 @default.
• W2949275670 cites W2145955247 @default.
• W2949275670 cites W2147339185 @default.
• W2949275670 cites W2149769193 @default.
• W2949275670 cites W2150029909 @default.
• W2949275670 cites W2150248355 @default.
• W2949275670 cites W2158698691 @default.
• W2949275670 cites W2159324718 @default.
• W2949275670 cites W2168341872 @default.
• W2949275670 cites W2198606573 @default.
• W2949275670 cites W2269649163 @default.
• W2949275670 cites W2294639133 @default.
• W2949275670 cites W2295745415 @default.
• W2949275670 cites W2336509392 @default.
• W2949275670 cites W2338148720 @default.
• W2949275670 cites W2345512687 @default.
• W2949275670 cites W2413160504 @default.
• W2949275670 cites W2433743436 @default.
• W2949275670 cites W2438305798 @default.
• W2949275670 cites W2584834072 @default.
• W2949275670 cites W2587659621 @default.
• W2949275670 cites W2594735672 @default.
• W2949275670 cites W2949588195 @default.
• W2949275670 doi "https://doi.org/10.1038/s41598-019-44966-x" @default.
• W2949275670 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/6559991" @default.
• W2949275670 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/31186519" @default.
• W2949275670 hasPublicationYear "2019" @default.
• W2949275670 type Work @default.
• W2949275670 sameAs 2949275670 @default.
• W2949275670 citedByCount "35" @default.
• W2949275670 countsByYear W29492756702018 @default.
• W2949275670 countsByYear W29492756702019 @default.
• W2949275670 countsByYear W29492756702020 @default.
• W2949275670 countsByYear W29492756702021 @default.
• W2949275670 countsByYear W29492756702022 @default.
• W2949275670 countsByYear W29492756702023 @default.
• W2949275670 crossrefType "journal-article" @default.
• W2949275670 hasAuthorship W2949275670A5062540383 @default.
• W2949275670 hasAuthorship W2949275670A5089051425 @default.
• W2949275670 hasBestOaLocation W29492756701 @default.
• W2949275670 hasConcept C102392041 @default.
• W2949275670 hasConcept C104317684 @default.
• W2949275670 hasConcept C108583219 @default.
• W2949275670 hasConcept C111919701 @default.
• W2949275670 hasConcept C119857082 @default.
• W2949275670 hasConcept C125411270 @default.
• W2949275670 hasConcept C147168706 @default.
• W2949275670 hasConcept C153180895 @default.
• W2949275670 hasConcept C154945302 @default.
• W2949275670 hasConcept C2778751112 @default.
• W2949275670 hasConcept C41008148 @default.
• W2949275670 hasConcept C50644808 @default.
• W2949275670 hasConcept C51679486 @default.
• W2949275670 hasConcept C54355233 @default.
• W2949275670 hasConcept C552990157 @default.
• W2949275670 hasConcept C66746571 @default.
• W2949275670 hasConcept C70721500 @default.
• W2949275670 hasConcept C81363708 @default.
• W2949275670 hasConcept C86803240 @default.
• W2949275670 hasConceptScore W2949275670C102392041 @default.
• W2949275670 hasConceptScore W2949275670C104317684 @default.
• W2949275670 hasConceptScore W2949275670C108583219 @default.
• W2949275670 hasConceptScore W2949275670C111919701 @default.
• W2949275670 hasConceptScore W2949275670C119857082 @default.
• W2949275670 hasConceptScore W2949275670C125411270 @default.
• W2949275670 hasConceptScore W2949275670C147168706 @default.
• W2949275670 hasConceptScore W2949275670C153180895 @default.
• W2949275670 hasConceptScore W2949275670C154945302 @default.
• W2949275670 hasConceptScore W2949275670C2778751112 @default.
• W2949275670 hasConceptScore W2949275670C41008148 @default.
• W2949275670 hasConceptScore W2949275670C50644808 @default.
• W2949275670 hasConceptScore W2949275670C51679486 @default.
• W2949275670 hasConceptScore W2949275670C54355233 @default.
• W2949275670 hasConceptScore W2949275670C552990157 @default.

• next