SIMULE (Index of Posts):


W-SIMULE

Tool W-SIMULE: A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs

We are updating the R package: simule with one more function: W-SIMULE

install.packages("simule")
library(simule)
demo(wsimuleDemo)

Package Manual

GitHub

Paper: @Arxiv @ NIPS 2017 workshop for Advances in Modeling and Learning Interactions from Complex Data.

Presentation: @Slides

Poster: @PDF

Abstract

Determining functional brain connectivity is crucial to understanding the brain and neural differences underlying disorders such as autism. Recent studies have used Gaussian graphical models to learn brain connectivity via statistical dependencies across brain regions from neuroimaging. However, previous studies often fail to properly incorporate priors tailored to neuroscience, such as preferring shorter connections. To remedy this problem, the paper here introduces a novel, weighted-ℓ1, multi-task graphical model (W-SIMULE). This model elegantly incorporates a flexible prior, along with a parallelizable formulation. Additionally, W-SIMULE extends the often-used Gaussian assumption, leading to considerable performance increases. Here, applications to fMRI data show that W-SIMULE succeeds in determining functional connectivity in terms of (1) log-likelihood, (2) finding edges that differentiate groups, and (3) classifying different groups based on their connectivity, achieving 58.6\% accuracy on the ABIDE dataset. Having established W-SIMULE’s effectiveness, it links four key areas to autism, all of which are consistent with the literature. Due to its elegant domain adaptivity, W-SIMULE can be readily applied to various data types to effectively estimate connectivity.

W-SIMULE

W-SIMULE

Citations

@article{singh2017constrained,
  title={A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of Heterogeneous Neural Connectivity Graphs},
  author={Singh, Chandan and Wang, Beilun and Qi, Yanjun},
  journal={arXiv preprint arXiv:1709.04090},
  year={2017}
}

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SIMULE R package is released!

Tool SIMULE: A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models

R package: simule

install.packages("simule")
library(simule)
demo(simuleDemo)

Package Manual

GitHub

Paper: @Arxiv | @Mach Learning

Talk

Abstract

Identifying context-specific entity networks from aggregated data is an important task, arising often in bioinformatics and neuroimaging. Computationally, this task can be formulated as jointly estimating multiple different, but related, sparse Undirected Graphical Models (UGM) from aggregated samples across several contexts. Previous joint-UGM studies have mostly focused on sparse Gaussian Graphical Models (sGGMs) and can’t identify context-specific edge patterns directly. We, therefore, propose a novel approach, SIMULE (detecting Shared and Individual parts of MULtiple graphs Explicitly) to learn multi-UGM via a constrained L1 minimization. SIMULE automatically infers both specific edge patterns that are unique to each context and shared interactions preserved among all the contexts. Through the L1 constrained formulation, this problem is cast as multiple independent subtasks of linear programming that can be solved efficiently in parallel. In addition to Gaussian data, SIMULE can also handle multivariate Nonparanormal data that greatly relaxes the normality assumption that many real-world applications do not follow. We provide a novel theoretical proof showing that SIMULE achieves a consistent result at the rate O(log(Kp)/n_{tot}). On multiple synthetic datasets and two biomedical datasets, SIMULE shows significant improvement over state-of-the-art multi-sGGM and single-UGM baselines.

SIMULE

Citations

@article{wang2016constrained,
  title={A constrained l1 minimization approach for estimating multiple Sparse Gaussian or Nonparanormal Graphical Models},
  author={Wang, Beilun and Singh, Ritambhara and Qi, Yanjun},
  journal={arXiv preprint arXiv:1605.03468},
  year={2016}
}

Support or Contact

Having trouble with our tools? Please contact Beilun and we’ll help you sort it out.