The website JointNets.org introduces a suite of tools we have developed for helping researchers effectively translate aggregated data into knowledge that take the form of graphs. This suite of toolboxes can have important biomedical applications, such as investigating molecular signatures corresponding to different drug treatments. It is expected to impact other domains as well, for instance, to identify condition-specific functional networks about human brain connectivity.

## Background: Sparse Gaussian Graphical Model (sGGM)

The sparse Gaussian Graphical Model(sGGM) assumes data samples are independently and identically drawn from a multivariate normal distribution with mean $\mu$ and covariance matrix $\Sigma$. The graph structure $G$ among $p$ features is encoded by the sparsity pattern of the inverse covariance matrix, also named precision matrix, $\Omega$.

In $G$ an edge does not connect $j$-th node and $k$-th node (i.e., conditional independent) if and only if $\Omega_{jk} = 0$. sGGM imposes a sparse L1 penalty on the $\Omega$.

## This website: Joint learning of Multiple Sparse Gaussian Graphical Model (multi-sGGM)

Modern multi-context molecular datasets are high dimensional, heterogeneous and noisy. For such heterogeneous data samples, rather than estimating sGGM of each condition separately, a multi-task formulation that jointly estimates $K$ different but related sGGMs can lead to a better generalization.

We have designed a suite of novel and fast machine-learning algorithms to identify context-specific interaction graphs from such data.

#### So far, we have released the following R packages:

No. Tool Name Short Description Venue
1 JEEK Fast~and~Scalable~Joint~Estimator~for **Integrating Additional Knowledge** in Learning Multiple Related Sparse Gaussian Graphical Models ICML18
2 DIFFEE Fast~and~Scalable Learning of **Sparse Changes** in High-Dimensional Gaussian Graphical Model Structure AISTAT18
3 FASJEM A Fast and Scalable Joint Estimator for Learning **Multiple Related** Sparse Gaussian Graphical Models AISTAT17
4 SIMULE A~constrained~L1~minimization~approach~for~estimating~multiple~Sparse **Gaussian or Nonparanormal** Graphical Models MachineLearning 17
5 W-SIMULE A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of **Heterogeneous Neural Connectivity** Graphs with Additional Prior knowledge NIPS17 Network workshop

## Contact

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