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Computing interaction probabilities in signaling networks
2015
Proceedings of the 6th ACM Conference on Bioinformatics, Computational Biology and Health Informatics - BCB '15
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In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. ...
Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. ...
Interaction probability in leukemia In this experiment, we explore the differences on interaction probabilities of signaling networks in distinct leukemia subtypes. ...
doi:10.1145/2808719.2814829
dblp:conf/bcb/GabrRGK15
fatcat:o5usj4z2lvczlbviei526ao4fy
Computing interaction probabilities in signaling networks
2015
EURASIP Journal on Bioinformatics and Systems Biology
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In this paper, we present a novel method for computing interaction probabilities in signaling networks based on transcription levels of genes. ...
Our method computes the interaction probabilities that minimize the gap between the observed and the computed signal reachability probabilities. ...
Interaction probability in leukemia In this experiment, we explore the differences on interaction probabilities of signaling networks in distinct leukemia subtypes. ...
doi:10.1186/s13637-015-0031-8
pmid:26587014
pmcid:PMC4642599
fatcat:6gyzwwa5gbhspfodphetalz43e
Signal reachability facilitates characterization of probabilistic signaling networks
2015
BMC Bioinformatics
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Our methods do this by computing the probability that a signal propagates successfully from receptor to reporter genes through interactions in the network. ...
Studying biological networks is of extreme importance in understanding cellular functions. These networks model interactions between molecules in each cell. ...
Computing reachability probability We use PReach [25] for measuring the signal reachability probability between receptors and reporters in probabilistic signaling networks. ...
doi:10.1186/1471-2105-16-s17-s6
pmid:26679404
pmcid:PMC4674881
fatcat:mvvcsvodovb7lmgoshhxk3wuvy
FROM UNCERTAIN PROTEIN INTERACTION NETWORKS TO SIGNALING PATHWAYS THROUGH INTENSIVE COLOR CODING
2012
Biocomputing 2013
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In this paper, we consider the problem of finding signaling pathways in protein interaction networks. ...
Discovering signaling pathways in protein interaction networks is a key ingredient in understanding how proteins carry out cellular functions. ...
Computational analysis of protein interaction networks has been essential in identification of signaling pathways. ...
doi:10.1142/9789814447973_0012
fatcat:g5d5jzybrvd33arlzti7juhpnm
Computational identification of signaling pathways in protein interaction networks
2015
F1000Research
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In this study, a computational technique to identify signaling pathways from a protein interaction network constructed using validated protein-protein interaction data was designed. ...
To construct a useful protein interaction network from this noisy data, computational methods are applied to validate the protein-protein interactions. ...
to protein-protein interaction data, creating a PPI network from the data and mining signaling pathways from the network. ...
doi:10.12688/f1000research.7591.1
fatcat:5e5jwdhkv5d27ihv7oc6htyu7a
Faspad: fast signaling pathway detection
2007
Computer applications in the biosciences : CABIOS
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FASPAD is a user-friendly tool that detects candidates for linear signaling pathways in protein interaction networks based on an approach by Scott et al. (Journal of Computational Biology, 2006). ...
can be found within seconds and with a 99.9% probability of optimality. ...
Computational Biology, NI 369/2) and PIAF (Fixed-Parameter Algorithms, NI 369/4). ...
doi:10.1093/bioinformatics/btm160
pmid:17463016
fatcat:cagq5z7cpraydawmdachuoheta
Biophysically based computational models of astrocyte ~ neuron coupling and their functional significance
2013
Frontiers in Computational Neuroscience
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Their paper also addresses potential computational functions of astrocyte-neuron interactions in the brain, in particular how astrocytes may enhance the computational power of neuronal networks in previously ...
This special topic presents nine papers which cover a range of issues from computational models of astrocyte-neuron interactions to the role of astrocytes in neurological disorders. ...
doi:10.3389/fncom.2013.00044
pmid:23675340
pmcid:PMC3646252
fatcat:blt2ov3545datcgqpjnh3sgux4
Medium access behavior analysis of two-flow topologies in IEEE 802.11 wireless networks
2016
EURASIP Journal on Wireless Communications and Networking
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Researchers have explored the MAC interaction of two-flow topologies in order to better understand the MAC behavior of nodes in generic multi-hop wireless network. ...
Twenty-five unique possible two-flow topologies can exist in general multi-hop wireless networks. ...
Competing interests The authors declare that we have competing interests with the following; Dr Saquib Razak, Assistant Teaching Professor, Computer Science, email address: srazak@qatar.cmu.edu. ...
doi:10.1186/s13638-016-0535-2
fatcat:nhkfua5f6zhh3aks64zduvp4hy
Human-computer interactive physical education teaching method based on speech recognition engine technology
2022
Frontiers in Public Health
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Students input through voice signals, and the system receives signals, analyzes signals, recognizes signals, and feeds back information to students in multiple forms. ...
Voice interaction is the most basic and practical type of human-computer interaction. ...
It is a parallel network structure based on window function (20) . The structure diagram of the probability and statistics neural network is shown in Figure 5 . ...
doi:10.3389/fpubh.2022.941083
pmid:35923977
pmcid:PMC9339716
fatcat:a3mjw6b7cjceffyn7oxvhvrrje
Biological network growth in complex environments: A computational framework
2020
PLoS Computational Biology
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We present here a computational framework based on directional statistics to model network formation in space and time under arbitrary spatial constraints. ...
Spatial biological networks are abundant on all scales of life, from single cells to ecosystems, and perform various important functions including signal transmission and nutrient transport. ...
Network growth as defined here is a parallel process by definition and thus can be computed in a distributed manner. ...
doi:10.1371/journal.pcbi.1008003
pmid:33253140
pmcid:PMC7728203
fatcat:g7gydy2wsnc27lx5ffxkcw2bam
Large scale analysis of signal reachability
2014
Computer applications in the biosciences : CABIOS
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We show that our method scales to entire human regulatory networks in only seconds, while the existing methods fail beyond a few tens of genes and interactions. ...
Results: We develop a novel and scalable method to compute the probability that a signal originating at any given set of source genes can arrive at any given set of target genes (i.e., transcription factors ...
For this we developed a fast, exact method for computing the probability for a signal to reach from a source node to a destination node in a probabilistic network. ...
doi:10.1093/bioinformatics/btu262
pmid:24932011
pmcid:PMC4058948
fatcat:uqpsukoga5by7euseb5grno53m
PReach
2007
Proceedings of the International Conference on Bioinformatics, Computational Biology and Biomedical Informatics - BCB'13
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In this paper, we develop a novel method, called PReach (Probabilistic Reachability), that precisely computes the probability that a signal can reach from a given collection of receptors to a given collection ...
of reporters when the underlying signaling network is uncertain. ...
SPINE uses the inclusion-exclusion method to compute the reachability probability without error in order to reconstruct signaling pathways. ...
doi:10.1145/2506583.2506586
dblp:conf/bcb/GabrTZDK13
fatcat:h45f47bgwzbotlhmez43tjcwpy
SigHotSpotter: scRNA-seq-based computational tool to control cell subpopulation phenotypes for cellular rejuvenation strategies
2019
Bioinformatics
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networks. ...
Here, we present SigHotSpotter, a computational tool to predict hotspots of signaling pathways responsible for the stable maintenance of cell subpopulation phenotypes, by integrating signaling and transcriptional ...
The node colors in the network represent the steady state probability of the signal to be in a specific node (signaling molecule). Inhibitory edges are shown in red and activation edges in green. ...
doi:10.1093/bioinformatics/btz827
pmid:31697324
pmcid:PMC7703776
fatcat:emzxlas32vbypc33h5bf5dskry
Perturbation Biology: Inferring Signaling Networks in Cellular Systems
2013
PLoS Computational Biology
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Computational network models are derived de novo, i.e., without prior knowledge of signaling pathways, and are based on simple non-linear differential equations. ...
We present a new experimental-computational technology of inferring network models that predict the response of cells to perturbations and that may be useful in the design of combinatorial therapy against ...
Acknowledgments We gratefully acknowledge the help of Deb Bemis in organizing and Ed Reznik in improving the manuscript. We thank Debora Marks, Doug ...
doi:10.1371/journal.pcbi.1003290
pmid:24367245
pmcid:PMC3868523
fatcat:veovpdsvyzep3pkusq5kdrdi3i
Representing dynamic biological networks with multi-scale probabilistic models
2019
Communications Biology
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ProbRules provides an avenue in current computational modeling by enabling systems biologists to integrate vast amounts of available data on different scales. ...
During signal transduction, molecular reactions and mechanisms occur in different spatial and temporal frames and involve feedbacks. ...
Wnt signaling is vital in different contexts, such as during embryonic development 44 and cancer 45 and thus can serve as an exemplary signal transduction network for computational investigation. ...
doi:10.1038/s42003-018-0268-3
pmid:30675519
pmcid:PMC6336720
fatcat:u7dyb4bf55h7lidbzx6rpy3mwy
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