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Response to "Comments on 'Bayesian variable selection for disease classification using gene expression data'"
2011
Bioinformatics
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of selected covariates, α is an intercept, β γ is a p γ ×1 vector of the corresponding regression coefficients and p γ is the number of selected covariates. ...
Following notations in Yang and Song (2010a) , Y is an ndimensional vector of binary observed random variables, Z is an n×1 vector of the underlying continuous latent variables, X γ is an n×p γ matrix ...
doi:10.1093/bioinformatics/btr334
pmid:21697131
fatcat:sihe5vdsbvd4rhbs47qlcg7lba
On Predicting lung cancer subtypes using 'omic' data from tumor and tumor-adjacent histologically-normal tissue
2016
BMC Cancer
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First, we used a feature selector (ReliefF/Limma) to select relevant variables, which were then used to build a simple naïve Bayes classification model. ...
Methods: Using publicly available datasets for gene expression and DNA methylation, we applied four classification tasks, depending on the possible combinations of tumor and TAHN tissue. ...
Bayesian networks [35] are particularly useful classifiers that are very popular in the classification of biomedical data. ...
doi:10.1186/s12885-016-2223-3
pmid:26944944
pmcid:PMC4778315
fatcat:ctnqsfkkabdipamjyzy6socjlm
Transcriptional network classifiers
2009
BMC Bioinformatics
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The validation of our classifier using clinical data demonstrates the promising value of our proposed approach for disease diagnosis. ...
Gene interactions play a central role in transcriptional networks. Many studies have performed genome-wide expression analysis to reconstruct regulatory networks to investigate disease processes. ...
A recent work proposes to use prior knowledge of known pathway information to select gene subnetworks as features for tissue classification [5] . ...
doi:10.1186/1471-2105-10-s9-s1
pmid:19761563
pmcid:PMC2745680
fatcat:q6le75pcsrfqddezjkakzaiwzi
Gene Selection in Arthritis Classification with Large-Scale Microarray Expression Profiles
2003
Comparative and Functional Genomics
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We achieve both goals simultaneously by combining a binary probit model for classification with Bayesian variable selection methods to identify important genes.We find very small sets of genes that lead ...
The use of large-scale microarray expression profiling to identify predictors of disease class has become of major interest. ...
Acknowledgements We thank Ilaria Dragoni, Novartis Institute for Medical Sciences, UK, and Don M. Wallace and Tracey C. ...
doi:10.1002/cfg.264
pmid:18629129
pmcid:PMC2447416
fatcat:myvltm3zdvespixnzlpa6pq3im
Bayesian networks classifiers for gene-expression data
2011
2011 11th International Conference on Intelligent Systems Design and Applications
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Second, we propose a preprocessing scheme for gene expression data, to induce Bayesian classifiers. ...
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 ...
Table I GENE I EXPRESSION DATA SETS FOR DIFFERENT TYPES OF CANCER USED IN OUR EXPERIMENTS. ...
doi:10.1109/isda.2011.6121822
dblp:conf/isda/CamposCCM11
fatcat:ijh5jeu4tvfe3jwg3oczcincnm
Cancer classification and prediction using logistic regression with Bayesian gene selection
2004
Journal of Biomedical Informatics
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In this paper, we propose a Bayesian approach to gene selection and classification using the logistic regression model. ...
After the important genes are identified, the same logistic regression model is then used for cancer classification and prediction. ...
selected using the proposed Bayesian gene selection algorithm for SRBCT data (p i = 15/n) Gene No. ...
doi:10.1016/j.jbi.2004.07.009
pmid:15465478
fatcat:rdnh7ngl3jgmlclch67tdzbemq
Bayesian Variable Selection in Multinomial Probit Models to Identify Molecular Signatures of Disease Stage
2004
Biometrics
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We apply our methodology to a problem in functional genomics using gene expression profiling data. ...
Our method makes use of mixture priors and Markov chain Monte Carlo techniques to select sets of variables that differ among the classes. ...
We thank Prof Hill Gaston for useful discussions and for providing the clinical samples. ...
doi:10.1111/j.0006-341x.2004.00233.x
pmid:15339306
fatcat:avqrep2or5firccgprkg5b5524
Bayesian Gene Selection Based on Pathway Information and Network-Constrained Regularization
2021
Computational and Mathematical Methods in Medicine
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High-throughput data make it possible to study expression levels of thousands of genes simultaneously under a particular condition. However, only few of the genes are discriminatively expressed. ...
In this paper, we proposed a Bayesian gene selection with a network-constrained regularization method, which can incorporate the pathway structural information as priors to perform gene selection. ...
Binary data such as absence or presence or different types of a disease are often used as response variables in gene selection problems. ...
doi:10.1155/2021/7471516
fatcat:n2gdgzs43fedfp7zsbd55axiaq
Integrating gene set analysis and nonlinear predictive modeling of disease phenotypes using a Bayesian multitask formulation
2016
BMC Bioinformatics
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gene expression data. ...
Results: In this study, we propose a Bayesian binary classification framework to integrate gene set analysis and nonlinear predictive modeling. ...
In the experiments reported, we use real-valued gene expression measurements as genomic data. ...
doi:10.1186/s12859-016-1311-3
pmid:28105911
pmcid:PMC5249028
fatcat:bllzdzxgyzestgfevzus7sw454
Development of sparse Bayesian multinomial generalized linear model for multi-class prediction
2014
BMC Bioinformatics
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Gene expression profiling has been used for many years to classify samples and to gain insights into the molecular mechanisms of phenotypes and diseases. ...
Taken together, these results suggest that the Bayesian Multinomial Probit model applied to cancer progression data allows for reasonable subclass prediction. ...
Acknowledgments This work was supported by the University of Memphis Center for Translational Informatics and the Assisi Foundation of Memphis. ...
doi:10.1186/1471-2105-15-s10-p14
pmcid:PMC4196030
fatcat:yyl5ocy2rfg77le2co6jauhfwq
Applications of Bayesian Gene Selection and Classification with Mixtures of Generalized Singular -Priors
2013
Computational and Mathematical Methods in Medicine
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The classification accuracy of our proposed model is higher with a smaller set of selected genes. ...
of genes to explore the relation between genes and disease. ...
Such an approach is useful in biomedical interpretations for the selection of relevant genes for disease of interest. ...
doi:10.1155/2013/420412
pmid:24382981
pmcid:PMC3870637
fatcat:xamsdrsrgfhvram63z47jguhoi
Application of Sparse Bayesian Generalized Linear Model to Gene Expression Data for Classification of Prostate Cancer Subtypes
2014
Open Journal of Statistics
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Taken together, these results suggest that sparse Bayesian Multinomial Probit model applied to cancer progression data allows for better subclass prediction and produces more functionally relevant gene ...
Initially, 398 genes were selected using ordinal logistic regression with a cutoff value of 0.05 after Benjamini and Hochberg FDR correction. ...
To address these limitations, we developed a sparse Bayesian multinomial model and evaluated its performance using prostate cancer gene expression data. ...
doi:10.4236/ojs.2014.47049
fatcat:wipknpetdjda7d2fdduyv2nahe
A Bayesian approach for inducing sparsity in generalized linear models with multi-category response
2015
BMC Bioinformatics
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The dimension and complexity of high-throughput gene expression data create many challenges for downstream analysis. ...
Conclusions: Using GDP in a Bayesian GLM model applied to cancer progression data results in better subclass prediction. ...
Acknowledgements This work and its publication was supported by the Billl & Melinda Gates Foundation and University of Memphis Center for Translational Informatics. ...
doi:10.1186/1471-2105-16-s13-s13
pmid:26423345
pmcid:PMC4597416
fatcat:rpmnyjudifbvpjt2m72tb7luli
Network-Based Identification of Biomarkers Coexpressed with Multiple Pathways
2014
Cancer Informatics
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Specifically, we evaluated implication networks, Boolean networks, Bayesian networks, and Pearson's correlation networks in constructing gene coexpression networks for identifying lung cancer diagnostic ...
Here, we give a comprehensive overview of computational methods used for biomarker identification, and provide a performance comparison of several network models used in studies of cancer susceptibility ...
Acknowledgments We thank Rebecca Raese for editing this paper.
Author contributions ...
doi:10.4137/cin.s14054
pmid:25392692
pmcid:PMC4218687
fatcat:kgxtqqip7nazpjckldlieaipgu
Temporal Bayesian classifiers for modelling muscular dystrophy expression data
2006
Intelligent Data Analysis
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In this paper we explore a new architecture of Bayesian classifier that can be used to understand how biological mechanisms differ with respect to time. ...
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. ...
In [17] , we explored the use of simple Bayesian classifiers for selecting genes that differentiate between different classes. ...
doi:10.3233/ida-2006-10504
fatcat:64f2f4fl3zfnlnfs5dgyq5hola
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