Name
Abstract | ||
|---|---|---|
The state of the art in bio-medical technologies has produced many genomic, epigenetic, transcriptomic, and proteomic data of varied types across different biological conditions. Historically, it has always been a challenge to produce new ways to integrate data of different types. Here, we leverage the node-conditional uni-variate exponential family distribution to capture the dependencies and interactions between different data types. The graph underlying our mixed graphical model contains both un-directed and directed edges. In addition, it is widely believed that incorporating data across different experimental conditions can lead us to a more holistic view of the biological system and help to unravel the regulatory mechanism behind complex diseases. We then integrate the data across related biological conditions through multiple graphical models. The performance of our approach is demonstrated through simulations and its application to cancer genomics.
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Year | Venue | Field |
|---|---|---|
2018 | BCB | Graph,Computer science,Exponential family,Lasso (statistics),Genomics,Data type,Artificial intelligence,Graphical model,Machine learning |
DocType | ISBN | Citations |
Conference | 978-1-4503-5794-4 | 0 |
PageRank | References | Authors |
0.34 | 3 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Christopher Ma | 1 | 2 | 1.79 |
| Tina Gui | 2 | 0 | 1.01 |
| Xin Dang | 3 | 139 | 9.85 |
| Yixin Chen | 4 | 0 | 0.34 |
| Dawn , Wilkins | 5 | 415 | 27.30 |