A deep aggregated model for protein secondary structure prediction

Hu, Yuxia; Nie, Tiezheng; Shen, Derong; Yu, Ge

doi:10.1504/ijdmb.2019.100624

Cited by 2 publications

(1 citation statement)

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“…DistR [28] was an efficient-distributed strategy to solve the problem of reachability query over large uncertain graphs that found all of the maximal subgraphs of an original graph on the step of distributed graph reduction and transform the problem into a relational join process on the step of distributed consolidation. Deep NBCN [29] discovered the homogeneous and multibranch architecture to model the complex internal relationship between amino acid sequence and protein secondary structure sequence.…”

Section: Dominant Nodes Dom(u)mentioning

confidence: 99%

Dominance-Partitioned Subgraph Matching on Large RDF Graph

Ning¹,

SunYunhao²,

ZhaoDeji³

et al. 2020

Complexity

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Subgraph matching on a large graph has become a popular research topic in the field of graph analysis, which has a wide range of applications including question answering and community detection. However, traditional edge-cutting strategy destroys the structure of indivisible knowledge in a large RDF graph. On the premise of load-balancing on subgraph division, a dominance-partitioned strategy is proposed to divide a large RDF graph without compromising the knowledge structure. Firstly, a dominance-connected pattern graph is extracted from a pattern graph to construct a dominance-partitioned pattern hypergraph, which divides a pattern graph as multiple fish-shaped pattern subgraphs. Secondly, a dominance-driven spectrum clustering strategy is used to gather the pattern subgraphs into multiple clusters. Thirdly, the dominance-partitioned subgraph matching algorithm is designed to conduct all isomorphic subgraphs on a cluster-partitioned RDF graph. Finally, experimental evaluation verifies that our strategy has higher time-efficiency of complex queries, and it has a better scalability on multiple machines and different data scales.

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Section: Dominant Nodes Dom(u)mentioning

confidence: 99%