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Frontiers in Bioscience, 2008
The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other ...
Huai, Li +3 more
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The discovery of regulatory networks is an important aspect in the post genomic research. The process requires integrated efforts of experimental and computational strategies by employing the systems biology approach. This review summarizes some of the major themes in computational inference of regulatory networks based on gene expression and other ...
Huai, Li +3 more
openaire +2 more sources
The construction of a fuzzy inference network by extension of the rule inference network
Neural Computing and Applications, 2005Fuzzy logic can bring about inappropriate inferences as a result of ignoring some information in the reasoning process. Neural networks are powerful tools for pattern processing, but are not appropriate for the logical reasoning needed to model human knowledge.
Malrey Lee, Tae-eun Kim
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INFERENCE OF GENE REGULATORY NETWORKS USING BOOLEAN-NETWORK INFERENCE METHODS
Journal of Bioinformatics and Computational Biology, 2009The modeling of genetic networks especially from microarray and related data has become an important aspect of the biosciences. This review takes a fresh look at a specific family of models used for constructing genetic networks, the so-called Boolean networks.
Graham J. Hickman, Charlie Hodgman
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Inference Networks for Document Retrieval
ACM SIGIR Forum, 1989The use of inference networks to support document retrieval is introduced. A network-basead retrieval model is described and compared to conventional probabilistic and Boolean models.
Howard R. Turtle, W. Bruce Croft
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Inferring Contagion in Regulatory Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2011Several gene regulatory network models containing concepts of directionality at the edges have been proposed. However, only a few reports have an interpretable definition of directionality. Here, differently from the standard causality concept defined by Pearl, we introduce the concept of contagion in order to infer directionality at the edges, i.e ...
André, Fujita +8 more
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On Network Topology Inference of Social Networks
2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), 2019This paper studies topology inference, from agent states, of a directed cyber-social network with opinion spreading dynamics model that explicitly takes confirmation bias into account. The cyber-social network comprises a set of partially connected directed network of agents at the social level, and a set of information sources at the cyber layer.
Yanbing Mao, Emrah Akyol
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Nonmonotonic Inferences and Neural Networks
Synthese, 2004zbMATH Open Web Interface contents unavailable due to conflicting licenses.
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Robust network topology inference
2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2017We address the problem of identifying a graph structure from the observation of signals defined on its nodes. Fundamentally, the unknown graph encodes direct relationships between signal elements, which we aim to recover from observable indirect relationships generated by a diffusion process on the graph. We put forth a novel network topology inference
Santiago Segarra +3 more
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Bayesian network loss inference
2003 IEEE International Conference on Acoustics, Speech, and Signal Processing, 2003. Proceedings. (ICASSP '03)., 2004In large-scale dynamic communication networks, endsystems can not rely on the network itself to cooperate in characterizing its own behavior. This has prompted research activities on methods for inferring internal network behavior based on the external end-to-end network measurements.
Dong Guo 0003, Xiaodong Wang 0001
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Guided Attention Inference Network
IEEE Transactions on Pattern Analysis and Machine Intelligence, 2020With only coarse labels, weakly supervised learning typically uses top-down attention maps generated by back-propagating gradients as priors for tasks such as object localization and semantic segmentation. While these attention maps are intuitive and informative explanations of deep neural network, there is no effective mechanism to manipulate the ...
Kunpeng Li +4 more
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