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Implementation of mutual information and bayes theorem for classification microarray data
2018
Journal of Physics, Conference Series
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The experiment results show that system is good to classify Microarray data with highest F1-score using Bayesian Network by 91.06%, and Naïve Bayes by 88.85%. ...
Preparation of microarray data is a huge problem and takes a long time. That is because microarray data contains high number of insignificant and irrelevant attributes. ...
Bayesian Network obtained 0.9106 average score. That score is good for classification of microarray data and used to detect cancer. ...
doi:10.1088/1742-6596/971/1/012011
fatcat:5a5uo54khvcgjigvsjbjbddk64
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
2006
Intelligent Data Analysis
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The analysis of microarray data from time-series experiments requires specialised algorithms, which take the temporal ordering of the data into account. ...
We show that this classifier improves the classification of microarray data and at the same time ensures that the models can easily be analysed by biologists by incorporating time transparently. ...
We aim to improve the classification of time-series microarray data using new forms of Bayesian classifiers, whilst at the same time ensuring that the models can easily be analysed by biologists by using ...
doi:10.3233/ida-2006-10504
fatcat:64f2f4fl3zfnlnfs5dgyq5hola
Bayesian networks classifiers for gene-expression data
2011
2011 11th International Conference on Intelligent Systems Design and Applications
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In this work, we study the application of Bayesian networks classifiers for gene expression data in three ways: first, we made an exhaustive state-of-art of Bayesian classifiers and Bayesian classifiers ...
induced from microarray data. ...
[39] also use Bayesian networks. They build an average network using different features orderings to avoid overfitting. Trying to represent time-series data with classifiers, Tucker et al. ...
doi:10.1109/isda.2011.6121822
dblp:conf/isda/CamposCCM11
fatcat:ijh5jeu4tvfe3jwg3oczcincnm
Discovery of Regulatory Connections in Microarray Data
[chapter]
2004
Lecture Notes in Computer Science
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In this paper, we introduce a new approach for mining regulatory interactions between genes in microarray time series studies. ...
In particular, we introduce a new across-model sampling scheme for performing Markov Chain Monte Carlo sampling of probabilistic network classifiers. ...
For that purpose, we need to choose a suitable representation scheme for time series microarray data. ...
doi:10.1007/978-3-540-30116-5_16
fatcat:wlbkriexwfbbndluonfqy7hi7y
From genes to networks: in systematic points of view
2011
BMC Systems Biology
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Several manuscripts were devoted to derive gene pathway and network from time series gene expression data. ...
Due to the computational inefficiency of Bayesian network methods, Zhang and his colleagues [3] chose to use State Space Model (SSM) to construct the gene regulatory networks for time course microarray ...
doi:10.1186/1752-0509-5-s3-i1
pmid:22784614
pmcid:PMC3287563
fatcat:i3zrko2mefeydplqdj4uu2gw2m
Survey on Modelling Methods Applicable to Gene Regulatory Network
[article]
2013
arXiv
pre-print
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For inference of GRN, time series data provided by Microarray technology is used. ...
Gene Regulatory Network (GRN) plays an important role in knowing insight of cellular life cycle. ...
Method discussed in [18] overcomes the problem of Bayesian Network of neglecting time series data which is useful in identifying direction of regulatory interactions among genes. ...
arXiv:1310.2361v1
fatcat:hcg4qetvgrdcrg6tadhpxjk3ze
Estimating Sparse Gene Regulatory Networks Using a Bayesian Linear Regression
2010
IEEE Transactions on Nanobioscience
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information-based method for the topranked Bayesian analysis of time series (a Bayesian user-friendly software for analyzing time-series microarray experiments) selected genes of the VAP dataset. ...
In this paper, we propose a gene regulatory network (GRN) estimation method, which assumes that such networks are typically sparse, using time-series microarray datasets. ...
Louis, for providing them valuable feedback to improve this paper. They also would like to thank the anonymous reviewers for their constructive comments. ...
doi:10.1109/tnb.2010.2043444
pmid:20650703
fatcat:zdw7gg2f7bfzjeyrreetpznofy
Transcriptome Data Analysis for Cell Culture Processes
[chapter]
2011
Genomics and Systems Biology of Mammalian Cell Culture
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In the case of time-series microarray studies, data analysis is further complicated by the correlation between successive time points in a series. ...
In this review, we survey the methodologies used in the analysis of static and time-series microarray data, covering data pre-processing, identification of differentially expressed genes, profile pattern ...
Bayesian Networks Bayesian networks have recently emerged as promising approaches for inferring gene regulatory networks using microarray data. ...
doi:10.1007/10_2011_116
pmid:22194060
fatcat:n6pphee56nacvikknytt4v2eji
A novel parametric approach to mine gene regulatory relationship from microarray datasets
2010
BMC Bioinformatics
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Many algorithms have been developed to reconstruct gene regulatory networks based on microarray data. ...
Subsequently, we used the naïve Bayesian network to integrate these features as well as the functional coannotation between transcription factors and their target genes. ...
Secondly, a series of network models has been widely used, such as Boolean network [6] [7] [8] , naïve Bayesian network [9] , and dynamic Bayesian network [10] [11] [12] . ...
doi:10.1186/1471-2105-11-s11-s15
pmid:21172050
pmcid:PMC3024862
fatcat:l2qcdmbduvaa5fms6yv727ei4q
A survey of models for inference of gene regulatory networks
2013
Nonlinear Analysis: Modelling and Control
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The most commonly used GRNs models based on Boolean networks, Bayesian networks, relevance networks, differential and difference equations are described. ...
In this article, I present the biological backgrounds of microarray, ChIP-chip and ChIPSeq technologies and the application of computational methods in reverse engineering of gene regulatory networks ( ...
Dynamic Bayesian networks BNs can represent probabilistic relations between variables without time lags and their drawback is that they cannot deal with time series data [22] . ...
doi:10.15388/na.18.4.13972
fatcat:2jil4fn4svfjpfnbk7f3uzkmoq
Identifying drug active pathways from gene networks estimated by gene expression data
2005
Genome Informatics Series
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pathways on the estimated network with time course drug response microarray data. ...
Our method estimates regulatory relationships between genes as a gene network from microarray data of gene disruptions with a Bayesian network model, then identifies the drug affected genes and their regulatory ...
Aburatani for the useful suggestions about the drug response time course gene expression data. ...
pmid:16362921
fatcat:vu4zm5myifecnnt6y4yklmnyxy
Reconstructing gene regulatory networks from time-series microarray data
2005
Physica A: Statistical Mechanics and its Applications
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Various computational approaches have been proposed for modeling gene regulatory networks, such as Boolean network, differential equations and Bayesian network. ...
accuracy compared to dynamic Bayesian network. ...
regulatory network from time series gene expression data. ...
doi:10.1016/j.physa.2004.11.032
fatcat:aomzan4jwrev5ahewxykccfjie
Bayesian Inference of Genetic Regulatory Networks from Time Series Microarray Data Using Dynamic Bayesian Networks
2007
Journal of Multimedia
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Reverse engineering of genetic regulatory networks from time series microarray data are investigated. We propose a dynamic Bayesian networks (DBNs) modeling and a full Bayesian learning scheme. ...
The estimated APPs provide useful information on the confidence of the inferred results and can also be used for efficient Bayesian data integration. ...
In this paper, we apply dynamic Bayesian networks (DBNs) to model the time series microarray experiment and develop a full Bayesian solution for learning the networks. ...
doi:10.4304/jmm.2.3.46-56
fatcat:s4iaah4xfbhcdlasumebkboxmu
Biological Data Mining
2008
Scientific Programming
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Unnikrishnan, applies frequent episode discovery to the task of inferring the underlying neuronal connectivity patterns from temporal multi-neuronal spike data streams, which comprise of symbolic time-series ...
The first paper, Semi-supervised learning for classification of protein sequence data, by Brian King and Chittibabu Guda, presents a com-prehensive evaluation of semi-supervised techniques for classifying ...
Unnikrishnan, applies frequent episode discovery to the task of inferring the underlying neuronal connectivity patterns from temporal multi-neuronal spike data streams, which comprise of symbolic time-series ...
doi:10.1155/2008/897294
fatcat:jwilfwskk5d7vkezm2xgjwyo3e
Machine learning in bioinformatics
2006
Briefings in Bioinformatics
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This article reviews machine learning methods for bioinformatics. ...
It presents modelling methods, such as supervised classification, clustering and probabilistic graphical models for knowledge discovery, as well as deterministic and stochastic heuristics for optimization ...
Acknowledgements The authors are grateful to the anonymous reviewers for their comments, which have helped us to greatly improve this article. ...
doi:10.1093/bib/bbk007
pmid:16761367
fatcat:4oss26occvhkjnetcr3sesnkcu
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