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Reconstructing Pathways in Large Genetic Networks from Genetic Perturbations
2004
Journal of Computational Biology
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As a by-product, the algorithm reconstructs all direct regulatory gene interactions in the network. The algorithm is recursive, and is built around a graph representation of genetic networks. ...
Its computational complexity is O(nk 2 ), where n is the number of genes in the network, and k is the average number of genes affected by a genetic perturbation. ...
I would like to thank the Santa Fe Institute for its continued support of my research program. ...
doi:10.1089/106652704773416885
pmid:15072688
fatcat:u7ovln32o5em5hbyrkwqixrzhi
How to reconstruct a large genetic network from n gene perturbations in fewer than n2 easy steps
2001
Bioinformatics
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To reconstruct a genetic network means to identify, for each gene and within the limits of experimental resolution, the direct effects of a perturbed gene on other genes. ...
It is based on a graph representation of a genetic network. Algorithmic complexity in both storage and time is low, less than O(n 2 ). ...
I would like to thank the Santa Fe Institute for its continued support of my research program, and two reviewers for their constructive comments. ...
doi:10.1093/bioinformatics/17.12.1183
pmid:11751227
fatcat:sejeszyvbjb7hloozphgebvl7e
Reconstruction of directed acyclic networks of dynamical systems
2013
2013 American Control Conference
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This amounts to assume static relationships among the measurements, making these techniques ill-suited for detecting propagative and dynamic phenomena that can be fundamental for the understanding of the ...
Knowledge of such a structure can aid in identifying cause and effect relationships, clustering of similar entities, detecting representative elements for an aggregate and determining reduced order models ...
The PC algorithm (see [11] , [14] ) is a well-known algorithm for the reconstruction of Directed Acyclic Graphs of random variables. ...
doi:10.1109/acc.2013.6580562
fatcat:a2kijpcc4rb5dfjmavoaymnh54
DAG-WGAN: Causal Structure Learning With Wasserstein Generative Adversarial Networks
[article]
2022
arXiv
pre-print
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2022 Embedded Systems and Applications unpublished doi:10.5121/csit.2022.120611 fatcat:bpdkz2bj6ffzndkzukpr4vrfne
and support the discovery of Directed Acyclic Graphs (DAGs) that faithfully represent the underlying data distribution. ...
Compared with other models, it scales well and handles both continuous and discrete data. Our experiments have evaluated DAG-WGAN against the state-of-the-art and demonstrated its good performance. ...
They are graphical models representing variables and their conditional dependencies in the form of directed acyclic graphs (DAG). ...
arXiv:2204.00387v1
fatcat:mrpxiupkrndnxi6ikucfzzbfni
Variational quantum eigensolver for causal loop Feynman diagrams and directed acyclic graphs
[article]
2023
arXiv
pre-print
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2022 pre-print arXiv:2210.13240v1 fatcat:ci3mi4yjf5anpklwpig3vqebraOther Versions
the selection of acyclic configurations in directed graphs. ...
A loop Hamiltonian based on the adjacency matrix describing a multiloop topology, and whose different energy levels correspond to the number of cycles, is minimized by VQE to identify the causal or acyclic ...
PID2020-114473GB-I00 and PID2019-105439GB-C22, and Generalitat Valenciana Grant No. PROMETEO/2021/071. ...
arXiv:2210.13240v3
fatcat:2pbhm2rcmjb3racgudhho5o7qy
Observability and reconstructibility of large-scale Boolean control networks via network aggregations
[article]
2017
arXiv
pre-print
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First, we define a special class of aggregations that are compatible with observability and reconstructibility (i.e, observability and reconstructibility are meaningful for each part of the aggregation ...
It is known that determining the observability and reconstructibility of Boolean control networks (BCNs) are both NP-hard in the number of nodes of BCNs. ...
Preliminaries
Boolean control networks A BCN is described by a directed graph that is called a network graph, and logical equations (e.g., Eqn. (1)), where a network graph consists of input nodes, state ...
arXiv:1704.03231v3
fatcat:gcfb7vrv2zfr3aphec52m3dg7q
Decoding Network Codes by Message Passing
[article]
2009
arXiv
pre-print
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Our message-passing framework provides a basis for the design of network codes and control of network topology with a view toward quantifiable complexity reduction in the sink terminals. ...
In this paper, we show how to construct a factor graph from a network code. This provides a systematic framework for decoding using message passing algorithms. ...
Let a directed acyclic graph, G = (N , L) model a communications network with nodes N and directed links L. ...
arXiv:0902.0417v2
fatcat:56ilpgiaqvdzroywhmtw4jrxum
A comprehensive survey of developmental programs reveals a dearth of tree-like lineage graphs and ubiquitous regeneration
2021
BMC Biology
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Instead, they are more likely to be directed acyclic graphs, with multiple lineages converging on the same terminal cell type. ...
Here, we use a generative model to test the limits to which such processes can be combined to generate multiple differentiation paths during development, and attempt to chart the diversity of multicellular ...
Acknowledgements We thank Luca Peliti, Albert Libchaber, Mukund Thattai, and John McBride for the useful discussions,and John McBride for the assistance with the writing of Python code for the analysis ...
doi:10.1186/s12915-021-01013-4
pmid:34020630
pmcid:PMC8140435
fatcat:ecpsm2webnblrbqoe75vuq6jc4
Characterizing bearing equivalence in directed graphs
[article]
2023
arXiv
pre-print
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For directed acyclic graphs, all eigenvalues of the associated bearing Laplacian are real and nonnegative, while for directed graphs containing cycles, the bearing Laplacian can have eigenvalues with negative ...
We then present several conditions to characterize bearing equivalence for both directed acyclic and cyclic graphs. ...
Minh Hoang Trinh for helpful discussions and inputs to this work. ...
arXiv:2303.05576v1
fatcat:fi4bchvxlzhjvk6njd54dbnmne
Toward a Comparative Cognitive History: Archimedes and D. H. J. Polymath
[article]
2012
arXiv
pre-print
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The first is the work of Archimedes of Syracuse, putatively an individual, and of other ancient Greek mathematicians. ...
To advance this proposed area of research, a small case study on the structure of argument and proof is presented. ...
ACKNOWLEDGEMENTS Structural data on the Polymath Project was graciously provided by Justin Cranshaw and Aniket Kittur. ...
arXiv:1204.3534v1
fatcat:n2ravwu33zdxpgy7ezwklhfhgi
Topology-Based Reconstruction Prevention for Decentralised Learning
[article]
2024
arXiv
pre-print
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We consider weak adversaries, who do not control the graph topology and can exploit neither the inner workings of the summation protocol nor the specifics of users' data. ...
The success rate depends only on the adversaries' direct neighbourhood, independent of the size of the full network. ...
We analysed the structural dependencies of the underlying network graph that permit this attack, and proved that acyclic networks are invulnerable to the reconstruction attack. ...
arXiv:2312.05248v2
fatcat:hhacm354pndsbh3nigxkilbdry
Reconstructing Causal Biological Networks through Active Learning
2016
PLoS ONE
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Our method generally leads to faster recovery of underlying network structure and faster convergence to final distribution of confidence scores over candidate graph structures using the full data, in comparison ...
However, continuous Bayesian networks are of great practical interest, especially in the study of complex biological systems and their quantitative properties. ...
Let X ¼ fX 1 ; Á Á Á ; X n g be a set of random variables and G ¼ ðX ; EÞ be a directed acyclic graph (DAG) over X , where ði; jÞ 2 E if and only if there is a directed edge from X i to X j . ...
doi:10.1371/journal.pone.0150611
pmid:26930205
pmcid:PMC4773135
fatcat:a3jlgo2mo5fnbjk2fauod6nhc4
D'ya like DAGs? A Survey on Structure Learning and Causal Discovery
[article]
2021
arXiv
pre-print
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We provide a review of background theory and a survey of methods for structure discovery. ...
Causal reasoning is a crucial part of science and human intelligence. In order to discover causal relationships from data, we need structure discovery methods. ...
Acyclic Directed Mixed Graphs (AGMGs) and Maximal Ancestral Graphs (MAGs) and -separation In the presence of unobserved confounding, an Acyclic Directed Mixed Graph (ADMG) may be used. ...
arXiv:2103.02582v2
fatcat:x45blijl5ze5xjyuqh6vlc26oq
Introduction to Graphical Modelling
[chapter]
2011
Handbook of Statistical Systems Biology
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In the first part (Sections 12.1, 12.2 and 12.3) we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation ...
by graphs. ...
The undirected graph is a simple 3 node chain, while the directed acyclic graphs are the converging, serial and diverging connections (collectively known as fundamental connections in the theory of Bayesian ...
doi:10.1002/9781119970606.ch11
fatcat:zklebqdbqzhzdgabnyglbveunm
Introduction to Graphical Modelling
[article]
2011
arXiv
pre-print
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In the first part we will provide a brief overview of the mathematical and statistical foundations of graphical models, along with their fundamental properties, estimation and basic inference procedures ...
The aim of this chapter is twofold. ...
The undirected graph is a simple 3 node chain, while the directed acyclic graphs are the converging, serial and diverging connections (collectively known as fundamental connections in the theory of Bayesian ...
arXiv:1005.1036v3
fatcat:uw32ias2rjenjnritssymprdpi
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