Recovering time-varying networks of dependencies in social and biological studies

By Amr Ahmed, Eric P. Xing. 2009

282 citations recorded by the source corpus.

Subfields: Molecular Biology, Statistical and Nonlinear Physics

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Citations in the corpus

On the Convexity of Latent Social Network Inference (2010, methodology): “Recently, grap hic l Lasso methods [25, 21, 6, 19] for static sparse graph estimation and extensions to time evolv ing graphical models [1, 8, 22] have been proposed with lots of success.”
The joint graphical lasso for inverse covariance estimation across multiple classes (2011, methodology): “Some proposals have used time-series data to deﬁne time-varying networks in the context of continuous or binary data (Zhou et al. 2008, Song et al. 2009 a , Ahmed & Xing 2009, Kolar & Xing 2009, Song et al. 2009 b , Kolar et al. 2010).”
Wisdom of crowds for robust gene network inference (2012, methodology): “(2)) using an inflation parameter of 3.”
Structure and dynamics of molecular networks: A novel paradigm of drug discovery: A comprehensive review (2012, unknown): “…Yeung et al., 2002; Segal et al., 2003; Tegnér et al., 2003; Friedman, 2004; Tegnér & Bjorkegren, 2007; Cosgrove et al., 2008; Kim et al., 2008; Ahmed & Xing, 2009; Stokic et al., 2009; Marbach et al., 2010, 2012; Yip et al., 2010; Pham et al., 2011; Schaffter et al., 2011; Altay, 2012;…”
Time-Varying Dynamic Bayesian Networks (2009, background): “Recently, there has been a surge of interests along this direction [5, 6, 7, 8, 9, 10 ].”
SLEP: Sparse Learning with Efficient Projections (2011, background): “It has found applications in comparative genomic hybridization [60, 71], prostate cancer analysis [70], image denoising [13], and time-varying networks [1], where features can be ordered in some meaningful way.”
An efficient algorithm for a class of fused lasso problems (2010, methodology): “It has found applications in comparative genomic hybridization [25, 31], prostate cancer analysis [30], image denoising [8], and time-varying networks [1], where features can be ordered in some meaningful way.”
Statistical inference of the time-varying structure of gene-regulation networks (2010, methodology): “We demonstrate the performance of the ARTIVA algorithm by ( i ) applying it to simulated data (Table 1) and ( ii ) performing a comparative analysis of the ARTIVA and TESLA [15] results (Figure 3).”
A Survey of Link Prediction in Social Networks (2011, background): “[5] proposed an scalable solution to a slightly different problem from link prediction, where they nd how links in the network vary over time.”