Technology revolutions in the past decade have collected large-scale heterogeneous samples from many scientific domains. For instance, genomic technologies have delivered petabytes of molecular measurements across more than hundreds of types of cells and tissues from national projects like ENCODE and TCGA. Neuroimaging technologies have generated petabytes of functional magnetic resonance imaging (fMRI) datasets across thousands of human subjects (shared publicly through projects like openfMRI). Given such data, understanding and quantifying variable graphs from heterogeneous samples (about multiple contexts) is a fundamental analysis task.
Such variable graphs can significantly simplify network-driven studies about diseases, can help understand the neural characteristics underlying clinical disorders and can allow for understanding genetic or neural pathways and systems. The number of contexts (denoted as $K$) that those applications need to consider grows extremely fast, ranging from tens (e.g., cancer types in TCGA) to thousands (e.g., number of subjects in openfMRI~). The number of variables (denoted as $p$) ranges from hundreds (e.g., number of brain regions) to tens of thousands (e.g., number of human genes).
One typical approach to tackle the above data analysis problem is to jointly estimate $K$ different but related conditional dependency graphs through a multi-task formulation of the sparse Gaussian Graphical Model (multi-sGGM). Most current studies of multi-sGGMs, however, involve expensive and difficult non-smooth optimizations, making them difficult to scale up to many dimensions (large $p$) or with many contexts (large $K$).
We aim to fill this gap and have designed a category of novel estimators that can achieve fast and scalable joint structure estimation of multiple sGGMs. There exist four important tasks when learning multi-sGGMs from heterogeneous samples:
Targeting each challenge, our work introduces estimators that are both computationally efficient and theoretically guaranteed. The website JointNets.org introduces the suite of tools we have developed for helping researchers effectively translate aggregated data into knowledge that take the form of graphs.
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 $\Omega$.
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.
No. | Tool Name | Short Description | Venue |
---|---|---|---|
1 | SIMULE | A~constrained~L1~minimization~approach~for~estimating~multiple~Sparse **Gaussian or Nonparanormal** Graphical Models | MachineLearning 17 |
2 | W-SIMULE | A Constrained, Weighted-L1 Minimization Approach for Joint Discovery of **Heterogeneous Neural Connectivity** Graphs with Additional Prior knowledge | NIPS17 Network workshop |
3 | JEEK | Fast~and~Scalable~Joint~Estimator~for **Integrating Additional Knowledge** in Learning Multiple Related Sparse Gaussian Graphical Models | ICML18 |
4 | FASJEM | A Fast and Scalable Joint Estimator for Learning **Multiple Related** Sparse Gaussian Graphical Models | AISTAT17 |
5 | DIFFEE | Fast~and~Scalable Learning of **Sparse Changes** in High-Dimensional Gaussian Graphical Model Structure | AISTAT18 |
6 | LaMP | Graph Neural Networks for Generating **Discriminative Relational Graphs** among Labels fpr Classification | ECML2018 |
6 | kDIFFNet | Adding Knowledge in DIFFEE Fast~and~Scalable Learning of **Sparse Changes** in High-Dimensional Gaussian Graphical Model Structure | UnderReview |
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