SILGGM: An extensive R package for efficient statistical inference in large-scale gene networks
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DOI: 10.1371/journal.pcbi.1006369
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- Zhou, Jia & Li, Yang & Zheng, Zemin & Li, Daoji, 2022. "Reproducible learning in large-scale graphical models," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
- Shilu Zhang & Saptarshi Pyne & Stefan Pietrzak & Spencer Halberg & Sunnie Grace McCalla & Alireza Fotuhi Siahpirani & Rupa Sridharan & Sushmita Roy, 2023. "Inference of cell type-specific gene regulatory networks on cell lineages from single cell omic datasets," Nature Communications, Nature, vol. 14(1), pages 1-25, December.
- Jinzhou Li & Marloes H. Maathuis, 2021. "GGM knockoff filter: False discovery rate control for Gaussian graphical models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 83(3), pages 534-558, July.
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