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Generating synthetic aging trajectories with a weighted network model using cross-sectional data

Research article (Scientific Reports, 2020) · cited 13× · AI/ML
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Generating synthetic aging trajectories with a weighted network model using cross-sectional data

Summary

Generating synthetic aging trajectories with a weighted network model using cross-sectional data is a scholarly article[1].

Key Facts

  • Generating synthetic aging trajectories with a weighted network model using cross-sectional data's instance of is recorded as scholarly article[2].

References

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Direct Wikidata claims

  1. [2] ↑ . wikidata.org.

Class ancestry

  1. [1] ↑ . Wikidata. wikidata.org.

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APA 4ort.xyz Knowledge Graph. (2026). Generating synthetic aging trajectories with a weighted network model using cross-sectional data. Retrieved May 24, 2026, from https://4ort.xyz/entity/generating-synthetic-aging-trajectories-with-a-weighted-network-model-using-cross-sectional-data
MLA “Generating synthetic aging trajectories with a weighted network model using cross-sectional data.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/generating-synthetic-aging-trajectories-with-a-weighted-network-model-using-cross-sectional-data.
BibTeX @misc{4ortxyz_generating-synthetic-aging-trajectories-with-a-weighted-network-model-using-cross-sectional-data_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Generating synthetic aging trajectories with a weighted network model using cross-sectional data}}, year = {2026}, url = {https://4ort.xyz/entity/generating-synthetic-aging-trajectories-with-a-weighted-network-model-using-cross-sectional-data}, note = {Accessed: 2026-05-24}}
LLM prompt According to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Generating synthetic aging trajectories with a weighted network model using cross-sectional data — https://4ort.xyz/entity/generating-synthetic-aging-trajectories-with-a-weighted-network-model-using-cross-sectional-data (retrieved 2026-05-24)

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