Results 1 to 10 of about 7,468,732 (318)
Network inference with hidden units
Mathematical Biosciences and Engineering, 2013 We derive learning rules for finding the connections between units in stochastic dynamical networks from the recorded history of a ``visible'' subset of the units. We consider two models. In both of them, the visible units are binary and stochastic. In
Joanna Tyrcha, John Hertz
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Network Inference With the Lasso. [PDF]
Multivariate Behav Res, 2022 Calculating confidence intervals and p-values of edges in networks is useful to decide their presence or absence and it is a natural way to quantify uncertainty. Since Lasso estimation is often used to obtain edges in a network, and the underlying distribution of Lasso estimates is discontinuous and has probability one at zero when the estimate is zero,
Waldorp L, Haslbeck J.
europepmc +3 more sources
Network Inference from Co-Occurrences [PDF]
IEEE Transactions on Information Theory, 2006 The recovery of network structure from experimental data is a basic and fundamental problem. Unfortunately, experimental data often do not directly reveal structure due to inherent limitations such as imprecision in timing or other observation mechanisms.
Figueiredo, Mario +2 more
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Network Inference from Consensus Dynamics [PDF]
2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017 We consider the problem of identifying the topology of a weighted, undirected network $\mathcal G$ from observing snapshots of multiple independent consensus dynamics.
Jadbabaie, Ali +2 more
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Bayesian networks for network inference in biology. [PDF]
J R Soc InterfaceBayesian networks (BNs) have been used for reconstructing interactions from biological data, in disciplines ranging from molecular biology to ecology and neuroscience. BNs learn conditional dependencies between variables, which best ‘explain’ the data, represented as a directed graph which approximates the relationships between variables. In the 2000s,
Hammond J, Smith VA.
europepmc +3 more sources
GTAT-GRN: a graph topology-aware attention method with multi-source feature fusion for gene regulatory network inference [PDF]
Frontiers in GeneticsGene regulatory network (GRN) inference is a central task in systems biology. However, due to the noisy nature of gene expression data and the diversity of regulatory structures, accurate GRN inference remains challenging. We hypothesize that integrating
Shuran Wang +17 more
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Network Plasticity as Bayesian Inference
PLOS Computational Biology, 2015 General results from statistical learning theory suggest to understand not only brain computations, but also brain plasticity as probabilistic inference. But a model for that has been missing.
Habenschuss, Stefan +3 more
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Inferring microbial co-occurrence networks from amplicon data: a systematic evaluation
mSystems, 2023 Microbes commonly organize into communities consisting of hundreds of species involved in complex interactions with each other. 16S ribosomal RNA (16S rRNA) amplicon profiling provides snapshots that reveal the phylogenies and abundance profiles of these
Dileep Kishore +5 more
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Network Cluster‐Robust Inference [PDF]
Econometrica, 2021 Since network data commonly consists of observations from a single large network, researchers often partition the network into clusters in order to apply cluster‐robust inference methods. Existing such methods require clusters to be asymptotically independent. Under mild conditions, we prove that, for this requirement to hold for network‐dependent data,
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Plugging Small RNAs into the Network
mSystems, 2020 Small RNAs (sRNAs) have been discovered in every bacterium examined and have been shown to play important roles in the regulation of a diverse range of behaviors, from metabolism to infection.
Lars Barquist
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