Network Discovery and Verification with Distance Queries

Selective network discovery via deep reinforcement learning on embedded spaces

Applied Network Science ◽

10.1007/s41109-021-00365-8 ◽

2021 ◽

Vol 6 (1) ◽

Author(s):

Peter Morales ◽

Rajmonda Sulo Caceres ◽

Tina Eliassi-Rad

Keyword(s):

Reinforcement Learning ◽

Learning Algorithm ◽

Sequential Decision ◽

Network Discovery ◽

Learning Tasks ◽

Partially Observed ◽

Decision Making Problem ◽

Resource Collection ◽

Improved Performance ◽

Discovery Algorithms

AbstractComplex networks are often either too large for full exploration, partially accessible, or partially observed. Downstream learning tasks on these incomplete networks can produce low quality results. In addition, reducing the incompleteness of the network can be costly and nontrivial. As a result, network discovery algorithms optimized for specific downstream learning tasks given resource collection constraints are of great interest. In this paper, we formulate the task-specific network discovery problem as a sequential decision-making problem. Our downstream task is selective harvesting, the optimal collection of vertices with a particular attribute. We propose a framework, called network actor critic (NAC), which learns a policy and notion of future reward in an offline setting via a deep reinforcement learning algorithm. The NAC paradigm utilizes a task-specific network embedding to reduce the state space complexity. A detailed comparative analysis of popular network embeddings is presented with respect to their role in supporting offline planning. Furthermore, a quantitative study is presented on various synthetic and real benchmarks using NAC and several baselines. We show that offline models of reward and network discovery policies lead to significantly improved performance when compared to competitive online discovery algorithms. Finally, we outline learning regimes where planning is critical in addressing sparse and changing reward signals.

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The Role of Network Science in Glioblastoma

Cancers ◽

10.3390/cancers13051045 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1045

Author(s):

Marta B. Lopes ◽

Eduarda P. Martins ◽

Susana Vinga ◽

Bruno M. Costa

Keyword(s):

Personalized Medicine ◽

Drug Development ◽

Information Flow ◽

Clinical Studies ◽

Network Science ◽

Cancer Genomics ◽

High Dimensional ◽

Network Discovery ◽

Software Implementations

Network science has long been recognized as a well-established discipline across many biological domains. In the particular case of cancer genomics, network discovery is challenged by the multitude of available high-dimensional heterogeneous views of data. Glioblastoma (GBM) is an example of such a complex and heterogeneous disease that can be tackled by network science. Identifying the architecture of molecular GBM networks is essential to understanding the information flow and better informing drug development and pre-clinical studies. Here, we review network-based strategies that have been used in the study of GBM, along with the available software implementations for reproducibility and further testing on newly coming datasets. Promising results have been obtained from both bulk and single-cell GBM data, placing network discovery at the forefront of developing a molecularly-informed-based personalized medicine.

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