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Regression Approaches for Microarray Data Analysis
2003
Journal of Computational Biology
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These concerns are exacerbated in the regression setting, where the objective is to relate gene expression, simultaneously for multiple genes, to some external outcome or phenotype. ...
., tumor versus normal tissue) of gene expression values as measured with microarrays. ...
ACKNOWLEDGMENTS We thank Trevor Hastie, Chih-Jen Lin, Chuck McCulloch, Adam Olshen, Rob Tibshirani, Berwin Turlach, Karen Vranizan, and Mu Zhu for software assistance and/or helpful comments. ...
doi:10.1089/106652703322756177
pmid:14980020
fatcat:ieai3cq5p5hibc6jdvd2nd2cpy
A Quasi-linear Approach for Microarray Missing Value Imputation
[chapter]
2011
Lecture Notes in Computer Science
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Missing value imputation for microarray data is important for gene expression analysis algorithms, such as clustering, classification and network design. ...
It may result from the fact that microarray data often comprises of huge size of genes with only a small number of observations, and nonlinear regression techniques are prone to overfitting. ...
Therefore, computational methods are desired to achieve accurate result for imputation of microarray data. ...
doi:10.1007/978-3-642-24955-6_28
fatcat:njblxgf37bhg5kyfctzhixg2eu
A Model-Based Joint Identification of Differentially Expressed Genes and Phenotype-Associated Genes
2016
PLoS ONE
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Over the last decade, many analytical methods and tools have been developed for microarray data. ...
Through analysis of data from experimental microarrays and simulation studies, the proposed model-based approach was shown to PLOS ONE | ...
Phenotype data for real data analysis. ...
doi:10.1371/journal.pone.0149086
pmid:26964035
pmcid:PMC4786130
fatcat:tcuyzn56m5fi5e7lihxkopbgfa
Principal Component Analysis in Linear Regression Survival Model with Microarray Data
2021
Journal of Data Science
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Compared with other model reduction techniques, the PCR approach is relatively insensitive to the number of covariates and hence suitable for high dimensional microarray data. ...
In this article, we study the linear regression survival model with right censored survival data, when high-dimensional microarray measurements are present. ...
Jian Huang and the referee for insightful suggestions. ...
doi:10.6339/jds.2007.05(2).326
fatcat:cjx6ktfwpvdzllj7nkp3qhc6ry
A Survey on Probabilistic Computational Model for Microarray Data Classification
2017
International Journal of Advanced Research in Computer Science and Software Engineering
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SUMMARY This survey presented the different approaches of the probabilistic classifier to solve the problem of microarray data classification. ...
Fig. 3 Classification accuracy for different microarray data set VII. ...
A plenitude of a probabilistic model for microarray data classification approaches have been designed by researchers, yet this paper implies that there are still many open opportunities for further improvement ...
doi:10.23956/ijarcsse/sv7i5/0221
fatcat:77ctmx7xg5hqhl5ca55ztleadm
A model selection approach to discover age-dependent gene expression patterns using quantile regression models
2009
BMC Genomics
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We show that our approach is robust in analyzing real and simulated datasets. We believe that our approach is applicable in many ageing or time-series data analysis tasks. ...
Results: Here we present a novel model selection approach to discover genes with linear or nonlinear age-dependent gene expression patterns from microarray data. ...
We thank Novi Quadrianto (NICTA) for introducing the basic idea of quantile regression to the first author. ...
doi:10.1186/1471-2164-10-s3-s16
pmid:19958479
pmcid:PMC2788368
fatcat:e65hyo46srehblv2k7k5lol6be
A Regression-Based Differential Expression Detection Algorithm for Microarray Studies with Ultra-Low Sample Size
2015
PLoS ONE
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Our method is easily adaptable for analysis of data from RNA-seq and other global expression experiments with low sample size and high dimensionality. ...
We propose a novel variable selection algorithm for ultra-low-n microarray studies using generalized linear model-based variable selection with a penalized binomial regression algorithm called penalized ...
Acknowledgments We thank Caroline Golino, Matthew Wong, and Andrew Halleran for their suggestions to the manuscript.
Author Contributions ...
doi:10.1371/journal.pone.0118198
pmid:25738861
pmcid:PMC4349782
fatcat:3naa2wkly5eernkoke57a77t7e
Partial least squares: a versatile tool for the analysis of high-dimensional genomic data
2006
Briefings in Bioinformatics
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Partial Least Squares (PLS) is a highly efficient statistical regression technique that is well suited for the analysis of high-dimensional genomic data. ...
Focusing on microarray expression data we provide a systematic comparison of the PLS approaches currently employed, and discuss problems as different as tumor classification, identification of relevant ...
The points (i) and (ii) render PLS methods very attractive for the analysis of microarray data. ...
doi:10.1093/bib/bbl016
pmid:16772269
fatcat:nnjyr7s7xvaazfndg6wziieftq
maSigPro: a method to identify significantly differential expression profiles in time-course microarray experiments
2006
Bioinformatics
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Motivation: Multi-series time-course microarray experiments are useful approaches for exploring biological processes. ...
The large amount of data, multiplicity of experimental conditions and the dynamic nature of the experiments poses great challenges to data analysis. ...
Furthermore the authors thank Francesco for his helpful comments. Conflict of Interest: none declared. ...
doi:10.1093/bioinformatics/btl056
pmid:16481333
fatcat:ke2skatbnnbaxmkys6ejkucjai
A model-based analysis of microarray experimental error and normalisation
2003
Nucleic Acids Research
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A statistical model is proposed for the analysis of errors in microarray experiments and is employed in the analysis and development of a combined normalisation regime. ...
Through analysis of the model and two-dye microarray data sets, this study found the following. ...
ACKNOWLEDGEMENTS We would like to thank Andrew Cossins, Andrew Gracey and Jane Fraser for providing the carp microarray data. ...
doi:10.1093/nar/gng097
pmid:12907748
pmcid:PMC169988
fatcat:uql62kfdezafpipbbuwemv2cwi
Statistical modelling of transcript profiles of differentially regulated genes
2008
BMC Molecular Biology
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Applying the regression modelling approach to microarray-derived time course data allowed 11% of the Escherichia coli features to be fitted by an exponential function, and 25% of the Rattus norvegicus ...
Through careful choice of appropriate model forms, such statistical regression approaches allow an improved comparison of gene expression profiles, and may provide an approach for the greater understanding ...
Acknowledgements Funding was provided by the UK Government Department for Food and Rural Affairs (DEFRA) project HH2116SMU. ...
doi:10.1186/1471-2199-9-66
pmid:18651954
pmcid:PMC2525656
fatcat:iogygymfcnh47b4ihd2h6kain4
Pathway-based Analysis with Support Vector Machine (SVM-LASSO) for Gene Selection and Classification
2017
International Journal on Advanced Science, Engineering and Information Technology
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This study proposed a pathway-based analysis for gene classification. Pathway-based analysis enables handling microarray data in order to improve biological interpretation of the analysis outcome. ...
Many methods have been done to address the issues of high data throughput due to increased use of microarray technology. ...
Linear regression is a commonly used approach in bioinformatics. ...
doi:10.18517/ijaseit.7.4-2.3397
fatcat:kqkocdkkknfjnfspgn5rf3a7j4
Meta-Analysis of Genomic Data: Between Strengths, Weaknesses and New Perspective
2016
International Journal of Biomedical Data Mining
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Meta-analysis of microarray data is one of the most common statistical techniques used for combining multiple data sets. ...
Here, we provided a briefly overview of current meta-analytic approaches together with the basic critical issues in performing meta-analysis of genomic data, with the aim of helping researchers to evaluate ...
An additional methodological approach for conducting a powerful meta-analysis uses the versatile method of the Elastic Net for classification and regression. ...
doi:10.4172/2090-4924.1000117
fatcat:vcjrwabe3faezfyo4au6jt7vii
Permutation-based adjustments for the significance of partial regression coefficients in microarray data analysis
2008
Genetic Epidemiology
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The application of a linear model is emphasized for data containing confounders and the permutationbased approaches are shown to be better suited for microarray data. ...
The aim of this paper is to generalize permutation methods for multiple testing adjustment of significant partial regression coefficients in a linear regression model used for microarray data. ...
Katerina Kechris for her helpful comments. ...
doi:10.1002/gepi.20255
pmid:17630650
pmcid:PMC2592303
fatcat:linjquzirjcydlczpyvoyfywpq
Evaluation of normalization methods for microarray data
2003
BMC Bioinformatics
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Normalization plays an important role in the earlier stage of microarray data analysis. The subsequent analysis results are highly dependent on normalization. ...
Microarray technology allows the monitoring of expression levels for thousands of genes simultaneously. ...
Thus, the evaluation of normalization methods in microarray data analysis is indeed an important issue. ...
doi:10.1186/1471-2105-4-33
pmid:12950995
pmcid:PMC200968
fatcat:2c2ylblg4zfidjpqjkpzoitbba
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