3,711 Hits in 7.3 sec


2000 Biocomputing 2001  
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doi:10.1142/9789814447362_0006 fatcat:eu3h4dharvfi7hqwz7lpr45x3q

A mixture model based approach for estimating the FDR in replicated microarray data

Shuo Jiao, Shun-Pu Zhang
2010 Journal of Biomedical Science and Engineering  
In this paper, we propose a new model based method as an improvement of the permutation based FDR estimation method of SAM [1] The new method uses the t-mixture model which can model the microarray data  ...  Finally, the proposed method is evaluated using extensive simulation and real microarray data.  ...  For the generated data, we calculate the true FDR and estimated FDR for a grid of total number of significant genes ranging from 100 to 1 (in decreasing order).  ... 
doi:10.4236/jbise.2010.33043 fatcat:vsqqung3pnff7eafe3jebnm6fm

Identifying drug active pathways from gene networks estimated by gene expression data

Yoshinori Tamada, Seiya Imoto, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
2005 Genome Informatics Series  
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  ...  We present a computational method for identifying genes and their regulatory pathways influenced by a drug, using microarray gene expression data collected by single gene disruptions and drug responses  ...  Aburatani for the useful suggestions about the drug response time course gene expression data.  ... 
pmid:16362921 fatcat:vu4zm5myifecnnt6y4yklmnyxy

ANMM4CBR: a case-based reasoning method for gene expression data classification

Bangpeng Yao, Shao Li
2010 Algorithms for Molecular Biology  
Moreover, in order to select the most informative genes, we propose to perform feature selection via additively optimizing a nonparametric margin maximum criterion, which is defined based on gene pre-selection  ...  Accurate classification of microarray data is critical for successful clinical diagnosis and treatment.  ...  Nan Chen in our laboratory for useful discussion and preprocessing of the data set.  ... 
doi:10.1186/1748-7188-5-14 pmid:20051140 pmcid:PMC2843690 fatcat:kzhkuxxc2fcsrcbrpuu6xdlmdi

Nonparametric pathway-based regression models for analysis of genomic data

Z. Wei, H. Li
2006 Biostatistics  
High-throughout genomic data provide an opportunity for identifying pathways and genes that are related to various clinical phenotypes.  ...  We propose to develop and evaluate a pathway-based gradient descent boosting procedure for nonparametric pathways-based regression(NPR) analysis to efficiently integrate genomic data and metadata.  ...  Acknowledgments This research was supported by NIH grant ES009911 and a grant from the Pennsylvania Department of Health. We thank Mr. Edmund Weisberg, MS at Penn CCEB for editorial assistance.  ... 
doi:10.1093/biostatistics/kxl007 pmid:16772399 fatcat:zl5a6i2ekfbjlek7bg3ptlfcmq

Statistical Approaches to Gene Mapping

Jurg Ott, Josephine Hoh
2000 American Journal of Human Genetics  
Electronic-Database Information The URLs for data in this article are as follows: Software (Laboratory for the Statistical Analysis of Microarray Data, Stanford University),  ...  Microarray data represent a new type of information that can provide important insight about the interaction of genes and that thus can complement the statistical approaches to gene mapping.  ...  Research into analytical methods and computer algorithms to facilitate the interpretation of microarray data is currently a very active area.  ... 
doi:10.1086/303031 pmid:10884361 pmcid:PMC1287177 fatcat:43ojqzbe6fe7npzuvrcv7sxuse

Learning transcriptional regulatory networks from high throughput gene expression data using continuous three-way mutual information

Weijun Luo, Kurt D Hankenson, Peter J Woolf
2008 BMC Bioinformatics  
Probability based statistical learning methods such as mutual information and Bayesian networks have emerged as a major category of tools for reverse engineering mechanistic relationships from quantitative  ...  We then used MI3 and control methods to infer a regulatory network centered at the MYC transcription factor from a published microarray dataset.  ...  Acknowledgements We thank Abhik Shah for helpful discussions in developing the nonparametric density estimator and Dr.  ... 
doi:10.1186/1471-2105-9-467 pmid:18980677 pmcid:PMC2613931 fatcat:nhfdheu5gncfvhvqlfpkzk3mmu

A Novel Feature Selection Algorithm using Particle Swarm Optimization for Cancer Microarray Data

Barnali Sahu, Debahuti Mishra
2012 Procedia Engineering  
The top scored genes from each cluster is gathered and a new feature subset is generated.  ...  This paper proposed a novel feature selection approach for the classification of high dimensional cancer microarray data, which used filtering technique such as signal-tonoise ratio (SNR) score and optimization  ...  Therefore our algorithm is a useful tool for selecting feature subset for cancer microarray data.  ... 
doi:10.1016/j.proeng.2012.06.005 fatcat:y7fxmj5vyvdkjp5y4xtfvffvom

A nonparametric empirical Bayes framework for large-scale multiple testing

R. Martin, S. t. Tokdar
2011 Biostatistics  
We propose a flexible and identifiable version of the two-groups model, motivated by hierarchical Bayes considerations, that features an empirical null and a semiparametric mixture model for the non-null  ...  This leads to a nonparametric empirical Bayes testing procedure, which we call PRtest, based on thresholding the estimated local false discovery rates.  ...  Ghosh for many helpful discussions. A portion of this work was completed while R. Martin was with the Department of Mathematical Sciences, Indiana University-Purdue University Indianapolis.  ... 
doi:10.1093/biostatistics/kxr039 pmid:22085895 fatcat:4kwuf4f6vzgs7o3zjzc2ldfuke

Practical Approaches to Analyzing Results of Microarray Experiments

Naftali Kaminski, Nir Friedman
2002 American Journal of Respiratory Cell and Molecular Biology  
In this article we provide a practically oriented review focusing on methods for analysis of large-scale gene expression data in the research laboratory.  ...  We discuss methods for scoring genes for their relevance , focusing on the statistical meaning of microarray results, especially with regard to the problem of multiple testing.  ...  N.K. is a recipient of a grant from the Tel-Aviv Chapter of the Israeli Lung Association.  ... 
doi:10.1165/ajrcmb.27.2.f247 pmid:12151303 fatcat:fq6tvmh5kbet3jnb7ikeswxfze

Gene networks as a tool to understand transcriptional regulation

Diogo Fernando Veiga, Fábio Fernandes da Rocha Vicente, Gustavo Bastos
2006 Genetics and Molecular Research  
GNs are built from the genome-wide high-throughput gene expression data that are often available from DNA microarray experiments.  ...  In the present study, we had two objectives: 1) to develop a framework for GN reconstruction based on a Bayesian network model that captures direct interactions between genes through nonparametric regression  ...  There is a special algorithm to learn the parameters from data (Eilers and Marx, 1996) .  ... 
pmid:16755516 fatcat:bffgbdm7a5dhnfxahcd5ihnjya

Building a Classifier for Integrated Microarray Datasets through Two-Stage Approach

Youngmi Yoon, Jongchan Lee, Sanghyun Park
2006 Sixth IEEE Symposium on BioInformatics and BioEngineering (BIBE'06)  
builds a classifier using only the informative genes.  ...  Since microarray data acquire tens of thousands of gene expression values simultaneously, they could be very useful in identifying the phenotypes of diseases.  ...  Table 1 shows an algorithm for identifying informative genes. To help understand the algorithm, let us assume that there is a microarray data as in Table 2 below.  ... 
doi:10.1109/bibe.2006.253321 dblp:conf/bibe/YoonLP06 fatcat:qqphlnbtcrfpln4xxy5dzrjyly

Noise filtering and nonparametric analysis of microarray data underscores discriminating markers of oral, prostate, lung, ovarian and breast cancer

Virginie M Aris, Michael J Cody, Jeff Cheng, James J Dermody, Patricia Soteropoulos, Michael Recce, Peter P Tolias
2004 BMC Bioinformatics  
Microarray technology has enabled marker discovery from human cells by permitting measurement of steady-state mRNA levels derived from thousands of genes.  ...  A major goal of cancer research is to identify discrete biomarkers that specifically characterize a given malignancy.  ...  Acknowledgments We thank Garret Hampton from the Genomics Institute of the Novartis Research Foundation for providing microarray data from three normal breast biopsies, and Gokce Toruner for his critical  ... 
doi:10.1186/1471-2105-5-185 pmid:15569388 pmcid:PMC538261 fatcat:leu3yvf7rbbfpjpf34jsqjfjci

Sequential prediction bounds for identifying differentially expressed genes in replicated microarray experiments

Robert D. Gibbons, Dulal K. Bhaumik, David R. Cox, Dennis R. Grayson, John M. Davis, Rajiv P. Sharma
2005 Journal of Statistical Planning and Inference  
We develop a new method for identifying differentially expressed genes in replicated cDNA and oligonucleotide microarray experiments.  ...  The method is used to identify gene expression levels that are associated with a pathological condition beyond chance expectations given the large number of genes tested.  ...  Tusher et al. (2001) propose a method known as significance analysis of microarrays (SAM) that assigns a score to each gene.  ... 
doi:10.1016/j.jspi.2004.06.037 fatcat:a3au7cvlrfgy3lzskkukgeaimq

Bayesian Network and Nonparametric Heteroscedastic Regression for Nonlinear Modeling of Genetic Network

Seiya Imoto, Sunyong Kim, Takao Goto, Sachiyo Aburatani, Kousuke Tashiro, Satoru Kuhara, Satoru Miyano
2003 Journal of Bioinformatics and Computational Biology  
We propose a new statistical method for constructing a genetic network from microarray gene expression data by using a Bayesian network.  ...  We consider fitting nonparametric regression models with heterogeneous error variances to the microarray gene expression data to capture the nonlinear structures between genes.  ...  Conclusion In this paper we proposed a new statistical method for estimating a genetic network from microarray gene expression data by using a Bayesian network and nonparametric regression.  ... 
doi:10.1142/s0219720003000071 pmid:15290771 fatcat:uuqwjaapf5bjzpd3l5rmji27ei
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