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Fuzzy logic based approaches for gene regulatory network inference
[article]
2018
arXiv
pre-print
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based approaches, probabilistic approaches (Bayesian networks, naive byes), artificial neural networks, and fuzzy logic. ...
Gene regulatory network (GRN) is a gene-gene interaction network which plays pivotal role in understanding gene regulation process and disease studies. ...
Fuzzy Logic based GRN inference The biological systems are very complex framework for modeling, describing describe different fuzzy based approach for GRN inference. ...
arXiv:1804.10775v1
fatcat:ct4yxzdq45ebdobxdsuwb3zlmy
State of the Art of Fuzzy Methods for Gene Regulatory Networks Inference
2015
The Scientific World Journal
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To address one of the most challenging issues at the cellular level, this paper surveys the fuzzy methods used in gene regulatory networks (GRNs) inference. ...
They provide viable computational tools for inferring GRNs from gene expression data, thus contributing to the discovery of gene interactions responsible for specific diseases and/orad hoccorrecting therapies ...
The method is used in evaluating numerous gene interactions for validating specific regulatory events based on filtering out the potential regulatory event candidates using fuzzy logic. ...
doi:10.1155/2015/148010
pmid:25879048
pmcid:PMC4386676
fatcat:kjdq6dmcwre4rp7vp2ye3hmeja
A Multistaged Hyperparallel Optimization of the Fuzzy-Logic Mechanistic Model of Molecular Regulation
[article]
2020
bioRxiv
pre-print
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This undermines the benefits inherent in the simplicity and strength of the fuzzy logic-based molecular regulatory inference approach. ...
logic regulatory model inference problem, particularly at higher order of interactions, quickly approaches those of computationally intractable problems. ...
Andrew Quong (formally at the Frederick National Laboratory for Cancer Research, and now at the Fluidigm Corporation), for mentoring the larger body of the work for which a component part is presented ...
doi:10.1101/2020.09.28.315986
fatcat:4fxbbpkg5vbrnkfdm5gifzsv6m
Identifying functional gene regulatory network phenotypes underlying single cell transcriptional variability
2015
Progress in Biophysics and Molecular Biology
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Here we present a novel fuzzy logic-based approach to infer quantitative gene regulatory network models from highly variable single-cell gene expression data. ...
Simulations of the inferred gene regulatory network response to experimentally observed stimuli levels mirrored the pattern and quantitative range of gene expression across individual neurons remarkably ...
Daniel Lees for his critical review of the paper and Mr. Hiren Makadia for facilitating the computational analyses. ...
doi:10.1016/j.pbiomolbio.2014.11.004
pmid:25433230
pmcid:PMC4366310
fatcat:eqmznll56ngpzdwlpk7kavmfnq
Evolutionary algorithms in genetic regulatory networks model
[article]
2012
arXiv
pre-print
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In this paper we have reviewed various evolutionary algorithms-based approach for modeling GRNs and discussed various opportunities and challenges. ...
Genetic Regulatory Networks (GRNs) plays a vital role in the understanding of complex biological processes. ...
Fuzzy logic has been successfully used for modeling gene regulatory networks due to its capability to represent nonlinear systems, its friendly language to incorporate and edit domain knowledge in the ...
arXiv:1205.1986v1
fatcat:sndsoua6cfbftpru4iv5jkxm6q
A Knowledge-guided Mechanistic Model of Synthetic Lethality in the HCT116 Vorinostat-resistant Colon Cancer Xenograft Model Cell-line
[article]
2021
bioRxiv
pre-print
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We employed a knowledege-guided fuzzy logic regulatory inference method to elucidate mechanistic relationships. We validated inferred regulatory models in independent datasets. ...
Reasoning that significant regulatory network genes are likely implicated in the clinical course of colorectal cancer, we show that the identified key regulatory network genes' expression profile are able ...
Saleet Jafri for their advises on the greater body of work presented here. ...
doi:10.1101/2021.06.22.449530
fatcat:adcanrahajcpzpstgso4enqax4
INFERENCE OF GENE REGULATORY NETWORKS FROM MICROARRAY DATA: A FUZZY LOGIC APPROACH
2005
Proceedings of the 4th Asia-Pacific Bioinformatics Conference
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In this paper, we present a novel fuzzy logic-based approach for this problem. ...
Before one can infer the structures of these networks, it is important to identify, for each gene in the network, which genes can affect its expression and how they affect it. ...
Conclusions In this paper, we have presented a novel fuzzy logic-based approach for the inference of GRNs. ...
doi:10.1142/9781860947292_0005
fatcat:p5fwkxm5p5g53icq2fcd4aas3m
An Integrated Framework to Model Cellular Phenotype as a Component of Biochemical Networks
2011
Advances in Bioinformatics
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We developed an approach based on fuzzy logic to model cell budding inSaccharomyces cerevisiaeusing time series expression microarray data of the cell cycle. ...
Computational models provide the means to characterize regulatory mechanisms and predict phenotype in the context of gene networks. ...
Of the methods described above, fuzzy logic-based approaches offer the proper balance between computational cost and biological interpretability for the specification of mechanistic transcriptional models ...
doi:10.1155/2011/608295
pmid:22190923
pmcid:PMC3235418
fatcat:w3734t7awzhavecbeykf6g4v4a
jFuzzyMachine – An Open-Source Fuzzy Logic-based Regulatory Inference Engine for High Throughput Biological Data
[article]
2020
bioRxiv
pre-print
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Here, we introduce jFuzzyMachine, a fuzzy logic based regulatory network inference engine for high throughput biological data. We describe its design and implementation. ...
We compared jFuzzyMachine inferred regulatory network to that inferred by the ARACNe (an Algorithm for the Reconstruction of Gene Regulatory Networks) tool. ...
It can be appreciated
Figure 4 : 4 The Fuzzy Logic-based Regulatory Network Inferred. A composite regulatory network is inferred from the best fitted models for each node. ...
doi:10.1101/2020.10.06.315994
fatcat:5ejhnpxaxzbqzosuddy4a4v6am
A New Approach for Modelling Gene Regulatory Networks Using Fuzzy Petri Nets
2010
Journal of Integrative Bioinformatics
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SummaryGene Regulatory Networks are models of genes and gene interactions at the expression level. ...
This method eliminates possible false predictions from the classical fuzzy model thereby allowing a wider search space for inferring regulatory relationship. ...
Acknowledgements The authors would like to thank the reviewers for their best comments that help improve the manuscript. Many thanks for my professor Syed I. Ahson for improving this paper. ...
doi:10.1515/jib-2010-113
fatcat:gfvxeeocgnaxjd3p5qxsfrt3ey
LogicNet: probabilistic continuous logics in reconstructing gene regulatory networks
2020
BMC Bioinformatics
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Most of studies apply fuzzy logics to infer the logical gene-gene interactions from continuous data. However, all these approaches require an a priori known network structure. ...
Gene Regulatory Networks (GRNs) have been previously studied by using Boolean/multi-state logics. ...
Soheil Jahangiri-Tazehkand for their helpful comments and suggestions. ...
doi:10.1186/s12859-020-03651-x
pmid:32690031
fatcat:yl3jyw6ravgphhvjjosv2p2exq
A new approach for modelling gene regulatory networks using fuzzy petri nets
2010
Journal of Integrative Bioinformatics
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Gene Regulatory Networks are models of genes and gene interactions at the expression level. ...
Fuzzy system has an ability to search microarray datasets for activator/repressor regulatory relationship. In this paper, we present a fuzzy reasoning model based on the Fuzzy Petri Net. ...
Acknowledgements The authors would like to thank the reviewers for their best comments that help improve the manuscript. Many thanks for my professor Syed I. Ahson for improving this paper. ...
doi:10.2390/biecoll-jib-2010-113
pmid:20134077
fatcat:hkir5muf6vcxjld7uifxpiz3my
Fuzzy Logic in Genetic Regulatory Network Models
2009
International Journal of Computers Communications & Control
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<p>Interactions between genes and the proteins they synthesize shape genetic regulatory networks (GRN). ...
The FIS is trained through an artificial neural network, which forms an Adaptive Nertwork-based Fuzzy Inference System (ANFIS). ...
can also be Fuzzy Logic in Genetic Regulatory Network Models 365 incorporated. ...
doi:10.15837/ijccc.2009.4.2453
fatcat:fodblaxn6nallbfxjxq55wlbue
Petri Nets with Fuzzy Logic (PNFL): Reverse Engineering and Parametrization
2010
PLoS ONE
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Methodology and Principal Findings: We inferred and parametrized simulation models based on Petri Nets with Fuzzy Logic (PNFL). ...
The in silico part of DREAM4 solicited the inference of cycle-rich gene regulatory networks from heterogeneous, noisy expression data including time courses as well as knockout, knockdown and multifactorial ...
Acknowledgments We acknowledge Florian Erhard for the stimulating discussions on the PNFL system. We thank all reviewers for their suggestions, which substantially improved the manuscript. ...
doi:10.1371/journal.pone.0012807
pmid:20862218
pmcid:PMC2942832
fatcat:kqm5nzfvqfe6tlpwt5qckttks4
Hybrid-Controlled Neurofuzzy Networks Analysis Resulting in Genetic Regulatory Networks Reconstruction
2012
ISRN Bioinformatics
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The approach uses fuzzy logic to transform gene expression values into qualitative descriptors that can be evaluated by using a set of defined rules. ...
The accuracy of the proposed approach is evaluated in terms of recall and F-score for the network reconstruction task. ...
In view of recent advances, a wide spectrum of reverse engineering approaches have been proposed to infer gene regulatory networks (GRNs), including: Boolean networks [17] [18] [19] ; Bayesian network ...
doi:10.5402/2012/419419
pmid:25969749
pmcid:PMC4393070
fatcat:bmjynvlvt5cula4m5oaxvv7nye
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