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Regression Approaches for Microarray Data Analysis

Mark R. Segal, Kam D. Dahlquist, Bruce R. Conklin
2003 Journal of Computational Biology  
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]

Yu Cheng, Lan Wang, Jinglu Hu
2011 Lecture Notes in Computer Science  
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

Samuel Sunghwan Cho, Yongkang Kim, Joon Yoon, Minseok Seo, Su-kyung Shin, Eun-Young Kwon, Sung-Eun Kim, Yun-Jung Bae, Seungyeoun Lee, Mi-Kyung Sung, Myung-Sook Choi, Taesung Park (+1 others)
2016 PLoS ONE  
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

Steven Ma
2021 Journal of Data Science  
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

Barnali Sahu, Ishara Priyadarsani
2017 International Journal of Advanced Research in Computer Science and Software Engineering  
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

Joshua WK Ho, Maurizio Stefani, Cristobal G dos Remedios, Michael A Charleston
2009 BMC Genomics  
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

Daniel Vasiliu, Samuel Clamons, Molly McDonough, Brian Rabe, Margaret Saha, Francisco J. Esteban
2015 PLoS ONE  
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

A.-L. Boulesteix, K. Strimmer
2006 Briefings in Bioinformatics  
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

A. Conesa, M. J. Nueda, A. Ferrer, M. Talon
2006 Bioinformatics  
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

Y. Fang
2003 Nucleic Acids Research  
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

Daniel C Eastwood, Andrew Mead, Martin J Sergeant, Kerry S Burton
2008 BMC Molecular Biology  
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

Nurul Athirah Nasrudin, Weng Howe Chan, Mohd Saberi Mohamad, Safaai Deris, Suhaimi Napis, Shahreen Kasim
2017 International Journal on Advanced Science, Engineering and Information Technology  
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

Spampinato AG, Cavallaro S
2016 International Journal of Biomedical Data Mining  
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

Brandie D. Wagner, Gary O. Zerbe, Sharon Mexal, Sherry S. Leonard
2008 Genetic Epidemiology  
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

Taesung Park, Sung-Gon Yi, Sung-Hyun Kang, SeungYeoun Lee, Yong-Sung Lee, Richard Simon
2003 BMC Bioinformatics  
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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