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Analysis of gene expression microarrays for phenotype classification

A Califano, G Stolovitzky, Y Tu
2000 Proceedings. International Conference on Intelligent Systems for Molecular Biology  
Given DNA-microarray data for a set of cells characterized by a given phenotype and for a set of control cells, an important problem is to identify "patterns" of gene expression that can be used to predict  ...  Several microarray technologies that monitor the level of expression of a large number of genes have recently emerged.  ...  The method described in this paper is a significant contribution to the set of tools for the analysis of gene expression microarray data.  ... 
pmid:10977068 fatcat:6t4mahpwn5dfvbfehmnsitttv4

Cross platform classification of microarrays by rank comparison

Sunho Lee
2015 Journal of the Korean Data and Information Science Society  
In this study, I limited the focus to the cross platform classification that the platform of a testing sample is different from the platform of a training set, and suggested a simple classification method  ...  Big data analysis pooling multiple data sets increases statistical power, improves the reliability of the results, and reduces the specific bias of the individual study.  ...  Existing classification methods The diagonal linear discriminant analysis (DLDA) Suppose that the class conditional gene expression profile X |m , a subset of X satisfying a class phenotype y = m (m =  ... 
doi:10.7465/jkdi.2015.26.2.475 fatcat:uhlnp26jivg3pc3io6gqi32mdq

Cross-platform analysis of cancer microarray data improves gene expression based classification of phenotypes

Patrick Warnat, Roland Eils, Benedikt Brors
2005 BMC Bioinformatics  
for the purpose of supervised classification analysis.  ...  Cross-platform classification of multiple cancer microarray data sets yields discriminative gene expression signatures that are found and validated on a large number of microarray samples, generated by  ...  Classification analysis For each pair of studies, classification analyses were performed on the UniGene matched gene expression values.  ... 
doi:10.1186/1471-2105-6-265 pmid:16271137 pmcid:PMC1312314 fatcat:tjabi22gufb6xer53vh35cyz6e

Two-transcript gene expression classifiers in the diagnosis and prognosis of human diseases

Lucas B Edelman, Giuseppe Toia, Donald Geman, Wei Zhang, Nathan D Price
2009 BMC Genomics  
The 'Top-Scoring Pair' (TSP) classification method identifies pairs of genes whose relative expression correlates strongly with phenotype.  ...  Identification of molecular classifiers from genome-wide gene expression analysis is an important practice for the investigation of biological systems in the post-genomic era -and one with great potential  ...  The work of DG was partially supported by NIH-NCCR Grant UL1 RR 025005 and NSF Grant CCF-0625687. Additional file 1  ... 
doi:10.1186/1471-2164-10-583 pmid:19961616 pmcid:PMC2797819 fatcat:s3vvz4vnozfzhj7cf6rfc3fg34

Prediction potential of candidate biomarker sets identified and validated on gene expression data from multiple datasets

Michael Gormley, William Dampier, Adam Ertel, Bilge Karacali, Aydin Tozeren
2007 BMC Bioinformatics  
ROC curves were used to assess the prediction error of each profile for classification.  ...  Independently derived expression profiles of the same biological condition often have few genes in common.  ...  Acknowledgements This study was supported by the National Institute of Health (NIH) grant #232240 and by the National science Foundation (NSF) grant # 235327.  ... 
doi:10.1186/1471-2105-8-415 pmid:17963508 pmcid:PMC2211325 fatcat:lobm7fofdbaova4n7dajg5rvfm

Expression profiles of switch-like genes accurately classify tissue and infectious disease phenotypes in model-based classification

Michael Gormley, Aydin Tozeren
2008 BMC Bioinformatics  
Large-scale compilation of gene expression microarray datasets across diverse biological phenotypes provided a means of gathering a priori knowledge in the form of identification and annotation of bimodal  ...  Use of a model-based clustering algorithm accurately classified more than 400 microarray samples into 19 different tissue types on the basis of bimodal gene expression.  ...  Acknowledgements This study was supported by the National Institute of Health (NIH) grant #232240 and by the National Science Foundation (NSF) grant # 235327.  ... 
doi:10.1186/1471-2105-9-486 pmid:19014681 pmcid:PMC2620272 fatcat:yuwhfkvdyfdunahib7tuvet4gy

Integrative disease classification based on cross-platform microarray data

Chun-Chi Liu, Jianjun Hu, Mrinal Kalakrishnan, Haiyan Huang, Xianghong Zhou
2009 BMC Bioinformatics  
In addition, we systematically map textual annotations of datasets to concepts in Unified Medical Language System (UMLS), permitting quantitative analysis of the phenotype "distance" between datasets and  ...  Disease classification has been an important application of microarray technology.  ...  Acknowledgements The authors thank Xuegong Zhang for helpful discussions and suggestions.  ... 
doi:10.1186/1471-2105-10-s1-s25 pmid:19208125 pmcid:PMC2648756 fatcat:romlcxl7xzgt3jdtzww5ks5tte

Relative Expression Analysis for Molecular Cancer Diagnosis and Prognosis

James A. Eddy, Jaeyun Sung, Donald Geman, Nathan D. Price
2010 Technology in Cancer Research and Treatment  
Relative Expression Analysis (RXA) is based only on the relative orderings among the expressions of a small number of genes.  ...  In the case of gene expression microarray data, standard statistical learning methods have been used to identify classifiers that can accurately distinguish disease phenotypes.  ...  The work of  ... 
doi:10.1177/153303461000900204 pmid:20218737 pmcid:PMC2921829 fatcat:lysyhi7qprdtjd2nmmfbdkb254


Hassan M. Fathallah-Shaykh
2005 Archives of Neurology  
The accuracy of fold changes is critical for data analysis.  ...  Current methods for microarray expression data analysis require numerous samples and yield measurements of low specificity. Kothapalli et al 23 examined microarray data from 2 different systems.  ... 
doi:10.1001/archneur.62.11.1669 pmid:16286538 fatcat:2f5ijjrxnjb4dn6pkwgptau6ii

Measuring the Effect of Inter-Study Variability on Estimating Prediction Error

Shuyi Ma, Jaeyun Sung, Andrew T. Magis, Yuliang Wang, Donald Geman, Nathan D. Price, Chuhsing Kate Hsiao
2014 PLoS ONE  
Results: As a case study, we gathered publicly available gene expression data from 1,470 microarray samples of 6 lung phenotypes from 26 independent experimental studies and 769 RNA-seq samples of 2 lung  ...  into classification.  ...  four of the experimental study datasets included in our analysis.  ... 
doi:10.1371/journal.pone.0110840 pmid:25330348 pmcid:PMC4201588 fatcat:5lu3eubejnha3ogv34asomjnb4

Class prediction models of thrombocytosis using genetic biomarkers

D. V. Gnatenko, W. Zhu, X. Xu, E. T. Samuel, M. Monaghan, M. H. Zarrabi, C. Kim, A. Dhundale, W. F. Bahou
2009 Blood  
Sex differences were rare in normal and ET cohorts (< 1% of genes) but were male-skewed for approximately 3% of RT genes.  ...  Subsequent quantitative RT-PCR analysis established that these biomarkers were 87.1% accurate in prospective classification of a new cohort.  ...  Acknowledgments The authors thank Xiao Wu for statistical analysis, Lesley Scudder and Jean Wainer for excellent technical support, and Dr Alexander Zuhoski for assistance with subject recruitment.  ... 
doi:10.1182/blood-2009-05-224477 pmid:19773543 pmcid:PMC2803693 fatcat:w5k4qz5lgvf7hbxuhxvwvbrefi

Integrative genomic and transcriptomic analysis of genetic markers in Dupuytren's disease

Junghyun Jung, Go Woon Kim, Byungjo Lee, Jong Wha J. Joo, Wonhee Jang
2019 BMC Medical Genomics  
The exact pathogenesis of DD remains unknown.  ...  In this study, we used weighted gene co-expression network analysis (WGCNA) to find co-expression gene set (module) of highly correlated genes for DD.  ...  Regulatory hotspot analysis To identify trans-regulatory hotspots, we performed GAMMA, one of multiple-phenotype analysis approaches to examine an association between a number of phenotypes or gene expression  ... 
doi:10.1186/s12920-019-0518-3 pmid:31296227 pmcid:PMC6624179 fatcat:jjlfxg7enzhqbcugoo2tl6ebga

Microarray-based expression profiling and informatics

Richard Simon
2008 Current Opinion in Biotechnology  
We review here the current state-of-the-art for design and analysis of microarray-based investigations.  ...  Microarray-based expression profiling is a powerful technology for studying biological mechanisms and for developing clinically valuable predictive classifiers.  ...  The current state of the literature with regard to analysis of microarray expression data is of serious concern [1] .  ... 
doi:10.1016/j.copbio.2007.10.008 pmid:18053704 pmcid:PMC2290821 fatcat:bhq3jrd4ynaafc2dkvzpia7lty

A graph-based representation of Gene Expression profiles in DNA microarrays

A. Benso, S. Di Carlo, G. Politano, L. Sterpone
2008 2008 IEEE Symposium on Computational Intelligence in Bioinformatics and Computational Biology  
This paper proposes a new and very flexible data model for gene expression analysis and classification called Gene Expression Graph (GEG).  ...  Microarrays provide simultaneous expression measurements for thousands of genes and facilitate the analysis of the complex relations among them.  ... 
doi:10.1109/cibcb.2008.4675762 dblp:conf/cibcb/BensoCPS08 fatcat:ls3epou23rcijhqhc6qghmniue

A new method for gene discovery in large-scale microarray data

K. Yano
2006 Nucleic Acids Research  
Microarrays are an effective tool for monitoring genome-wide gene expression levels.  ...  In current microarray analyses, the majority of genes on arrays are frequently eliminated for further analysis because the changes in their expression levels (ratios) are considered to be not significant  ...  The authors also acknowledge Naomi Ishida and Hironori Mizuguchi (BioInformatics Business Promotion Center, NEC Corporation) for the large-scale analyses of MeSH and GO terms using the NEC product 'BioCompass  ... 
doi:10.1093/nar/gkl058 pmid:16537840 pmcid:PMC1401514 fatcat:qq5l52aj3babjigjmnkhvrnsp4
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