FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4384920714> ?p ?o ?g. }

• W4384920714 abstract "The Hi-C experiments have been extensively used for the studies of genomic structures. In the last few years, spatiotemporal Hi-C has largely contributed to the investigation of genome dynamic reorganization. However, computationally modeling and forecasting spatiotemporal Hi-C data still have not been seen in the literature. We present HiC4D for dealing with the problem of forecasting spatiotemporal Hi-C data. We designed and benchmarked a novel network and named it residual ConvLSTM (ResConvLSTM), which is a combination of residual network and convolutional long short-term memory (ConvLSTM). We evaluated our new ResConvLSTM networks and compared them with the other five methods, including a naïve network (NaiveNet) that we designed as a baseline method and four outstanding video-prediction methods from the literature: ConvLSTM, spatiotemporal LSTM (ST-LSTM), self-attention LSTM (SA-LSTM) and simple video prediction (SimVP). We used eight different spatiotemporal Hi-C datasets for the blind test, including two from mouse embryogenesis, one from somatic cell nuclear transfer (SCNT) embryos, three embryogenesis datasets from different species and two non-embryogenesis datasets. Our evaluation results indicate that our ResConvLSTM networks almost always outperform the other methods on the eight blind-test datasets in terms of accurately predicting the Hi-C contact matrices at future time-steps. Our benchmarks also indicate that all of the methods that we benchmarked can successfully recover the boundaries of topologically associating domains called on the experimental Hi-C contact matrices. Taken together, our benchmarks suggest that HiC4D is an effective tool for predicting spatiotemporal Hi-C data. HiC4D is publicly available at both http://dna.cs.miami.edu/HiC4D/ and https://github.com/zwang-bioinformatics/HiC4D/." @default.
• W4384920714 created "2023-07-22" @default.
• W4384920714 creator A5034371028 @default.
• W4384920714 creator A5052267876 @default.
• W4384920714 date "2023-07-20" @default.
• W4384920714 modified "2023-09-26" @default.
• W4384920714 title "HiC4D: forecasting spatiotemporal Hi-C data with residual ConvLSTM" @default.
• W4384920714 cites W1969830384 @default.
• W4384920714 cites W1973062929 @default.
• W4384920714 cites W2064675550 @default.
• W4384920714 cites W2070021921 @default.
• W4384920714 cites W2090037139 @default.
• W4384920714 cites W2152623715 @default.
• W4384920714 cites W2163412235 @default.
• W4384920714 cites W2343039591 @default.
• W4384920714 cites W2481029670 @default.
• W4384920714 cites W2584394140 @default.
• W4384920714 cites W2604200375 @default.
• W4384920714 cites W2731989040 @default.
• W4384920714 cites W2734870846 @default.
• W4384920714 cites W2735035409 @default.
• W4384920714 cites W2765567136 @default.
• W4384920714 cites W2927575800 @default.
• W4384920714 cites W2932751272 @default.
• W4384920714 cites W2952203741 @default.
• W4384920714 cites W2952940726 @default.
• W4384920714 cites W2980272550 @default.
• W4384920714 cites W2982661386 @default.
• W4384920714 cites W2992741119 @default.
• W4384920714 cites W3015686080 @default.
• W4384920714 cites W3047847485 @default.
• W4384920714 cites W3125968884 @default.
• W4384920714 cites W3138340468 @default.
• W4384920714 cites W3162387515 @default.
• W4384920714 cites W3162873709 @default.
• W4384920714 cites W3171922606 @default.
• W4384920714 cites W3199362033 @default.
• W4384920714 cites W4283645363 @default.
• W4384920714 cites W4308038519 @default.
• W4384920714 doi "https://doi.org/10.1093/bib/bbad263" @default.
• W4384920714 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/37478379" @default.
• W4384920714 hasPublicationYear "2023" @default.
• W4384920714 type Work @default.
• W4384920714 citedByCount "0" @default.
• W4384920714 crossrefType "journal-article" @default.
• W4384920714 hasAuthorship W4384920714A5034371028 @default.
• W4384920714 hasAuthorship W4384920714A5052267876 @default.
• W4384920714 hasBestOaLocation W43849207141 @default.
• W4384920714 hasConcept C11413529 @default.
• W4384920714 hasConcept C119857082 @default.
• W4384920714 hasConcept C124101348 @default.
• W4384920714 hasConcept C153180895 @default.
• W4384920714 hasConcept C154945302 @default.
• W4384920714 hasConcept C155512373 @default.
• W4384920714 hasConcept C41008148 @default.
• W4384920714 hasConcept C81363708 @default.
• W4384920714 hasConceptScore W4384920714C11413529 @default.
• W4384920714 hasConceptScore W4384920714C119857082 @default.
• W4384920714 hasConceptScore W4384920714C124101348 @default.
• W4384920714 hasConceptScore W4384920714C153180895 @default.
• W4384920714 hasConceptScore W4384920714C154945302 @default.
• W4384920714 hasConceptScore W4384920714C155512373 @default.
• W4384920714 hasConceptScore W4384920714C41008148 @default.
• W4384920714 hasConceptScore W4384920714C81363708 @default.
• W4384920714 hasFunder F4320332161 @default.
• W4384920714 hasFunder F4320337354 @default.
• W4384920714 hasIssue "5" @default.
• W4384920714 hasLocation W43849207141 @default.
• W4384920714 hasLocation W43849207142 @default.
• W4384920714 hasLocation W43849207143 @default.
• W4384920714 hasOpenAccess W4384920714 @default.
• W4384920714 hasPrimaryLocation W43849207141 @default.
• W4384920714 hasRelatedWork W2521062615 @default.
• W4384920714 hasRelatedWork W2767651786 @default.
• W4384920714 hasRelatedWork W2912288872 @default.
• W4384920714 hasRelatedWork W2961085424 @default.
• W4384920714 hasRelatedWork W3016958897 @default.
• W4384920714 hasRelatedWork W3021430260 @default.
• W4384920714 hasRelatedWork W3027997911 @default.
• W4384920714 hasRelatedWork W3181746755 @default.
• W4384920714 hasRelatedWork W4287776258 @default.
• W4384920714 hasRelatedWork W4306674287 @default.
• W4384920714 hasVolume "24" @default.
• W4384920714 isParatext "false" @default.
• W4384920714 isRetracted "false" @default.
• W4384920714 workType "article" @default.