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| Open AccessNetBID2 provides comprehensive hidden driver analysis
It’s challenging to capture “hidden” drivers that may not be genetically-altered or differentially-expressed from omics data. Here the authors developed NetBID2, a comprehensive network-based toolbox with versatile features, enabling the integration of multi-omics data to expose such hidden drivers.
- Xinran Dong
- , Liang Ding
- & Jiyang Yu
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Article
| Open AccessData-driven learning how oncogenic gene expression locally alters heterocellular networks
While mechanistic models play increasing roles in immuno-oncology, hand network curation is current practice. Here the authors use a Bayesian data-driven approach to infer how expression of a secreted oncogene alters the cellular landscape within the tumor.
- David J. Klinke II
- , Audry Fernandez
- & Anika C. Pirkey
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Article
| Open AccessFinding gene network topologies for given biological function with recurrent neural network
Networks are useful ways to describe interactions between molecules in a cell, but predicting the real topology of large networks can be challenging. Here, the authors use deep learning to predict the topology of networks that perform biologically-plausible functions.
- Jingxiang Shen
- , Feng Liu
- & Chao Tang