Filters








8,466 Hits in 8.8 sec

Short time-series microarray analysis: Methods and challenges

Xuewei Wang, Ming Wu, Zheng Li, Christina Chan
2008 BMC Systems Biology  
The detection and analysis of steady-state gene expression has become routine.  ...  Current efforts have shown promise in improving the analysis of short time-series microarray data, although challenges remain.  ...  C.C is supported in part by the National Institute of Health (1R01GM079688-01), National Science Foundation (BES 0425821), and the MSU Foundation on the Center for Systems Biology.  ... 
doi:10.1186/1752-0509-2-58 pmid:18605994 pmcid:PMC2474593 fatcat:c5ro4kggd5e5nmzqdepc6m3y74

CbGRiTS: Cerebellar gene regulation in time and space

Thomas Ha, Douglas Swanson, Matt Larouche, Randy Glenn, Dave Weeden, Peter Zhang, Kristin Hamre, Michael Langston, Charles Phillips, Mingzhou Song, Zhengyu Ouyang, Elissa Chesler (+8 others)
2015 Developmental Biology  
We demonstrate the use of CbGRiTS dataset as a community resource to explore patterns of gene expression and develop hypotheses concerning gene regulatory networks in brain development. Crown  ...  The temporal information from time series transcriptome analysis can serve as a potent source of associative information between developmental processes and regulatory genes.  ...  The microarray data and qRT-PCR showed the similar temporal expression pattern over time.  ... 
doi:10.1016/j.ydbio.2014.09.032 pmid:25446528 fatcat:hugec223yncpndoynwqhkapgm4

The analytical landscape of static and temporal dynamics in transcriptome data

Sunghee Oh, Seongho Song, Nupur Dasgupta, Gregory Grabowski
2014 Frontiers in Genetics  
dependencies in gene expression patterns.  ...  Reduced sequencing costs have made feasible dense time-series analysis of gene expression using RNA-seq; however, statistical methods in the context of temporal RNA-seq data are poorly developed.  ...  We also appreciate critical comments to biological insights on this review from Neilson Derek and are grateful for editing and comments on manuscript to Sandy Grabowski and Reviewers of Journal.  ... 
doi:10.3389/fgene.2014.00035 pmid:24600473 pmcid:PMC3929947 fatcat:7rlhgcuasjhdxfvbxbxio2n2hy

Time Series Expression Analyses Using RNA-seq: A Statistical Approach

Sunghee Oh, Seongho Song, Gregory Grabowski, Hongyu Zhao, James P. Noonan
2013 BioMed Research International  
However, statistically rigorous and computationally efficient methods are needed to explore the time-dependent changes of gene expression in biological systems.  ...  These methods should explicitly account for the dependencies of expression patterns across time points.  ...  Acknowledgments This work was supported in part by NIH GM094780 (J. P. Noonan), GM59507 (H. Zhao) and NSF DMS 1106738 (H. Zhao).  ... 
doi:10.1155/2013/203681 pmid:23586021 pmcid:PMC3622290 fatcat:bojm6ntuwrg2vdzhz5dicq6rla

PEM: A GENERAL STATISTICAL APPROACH FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN TIME-COURSE CDNA MICROARRAY EXPERIMENT WITHOUT REPLICATE

Xu Han, Wing-Kin Sung, Lin Feng
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood.  ...  Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed.  ...  Model-specific approaches identify differentially expressed genes based on prior knowledge of their temporal patterns.  ... 
doi:10.1142/18609475730022 fatcat:6gazq655erbh3ow74r7wcz7ajq

PEM: A GENERAL STATISTICAL APPROACH FOR IDENTIFYING DIFFERENTIALLY EXPRESSED GENES IN TIME-COURSE CDNA MICROARRAY EXPERIMENT WITHOUT REPLICATE

Xu Han, Wing-Kin Sung, Lin Feng
2006 Computational Systems Bioinformatics - Proceedings of the Conference CSB 2006  
Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood.  ...  Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed.  ...  Model-specific approaches identify differentially expressed genes based on prior knowledge of their temporal patterns.  ... 
doi:10.1142/1860947573_0022 fatcat:3ab72lvfs5dfrp2nqej2mly52a

A statistical analysis of memory CD8 T cell differentiation: An application of a hierarchical state space model to a short time course microarray experiment

Haiyan Wu, Ming Yuan, Susan M. Kaech, M. Elizabeth Halloran
2007 Annals of Applied Statistics  
In this paper we investigate the differentiation of memory CD8 T cells in the immune response using a short time course microarray experiment.  ...  To analyze this CD8 T-cell experiment, we develop a hierarchical state space model so that we can: (1) detect temporally differentially expressed genes, (2) identify the direction of successive changes  ...  time points, and exhibiting patterns of temporal non-stationarity.  ... 
doi:10.1214/07-aoas118 fatcat:civmuvsa75clreb3ihuzb6lwyu

Pem: a general statistical approach for identifying differentially expressed genes in time-course cDNA microarray experiment without replicate

Xu Han, Wing-Kin Sung, Lin Feng
2006 Computational systems bioinformatics. Computational Systems Bioinformatics Conference  
Besides, modeling the temporal expression patterns is difficult when the dynamics of gene expression in the experiment is poorly understood.  ...  Replication of time series in microarray experiments is costly. To analyze time series data with no replicate, many model-specific approaches have been proposed.  ...  For differentially expressed genes, the signal component is one of the three frequently observed signal patterns in time course microarray data, as shown in Fig. 3(a) .  ... 
pmid:17369631 fatcat:3xxhgfhz7jhrrclj6ulynfitgi

Discovering Biological Progression Underlying Microarray Samples

Peng Qiu, Andrew J. Gentles, Sylvia K. Plevritis, Jennifer L. Reed
2011 PLoS Computational Biology  
We present a novel computational approach, called Sample Progression Discovery (SPD), to discover patterns of biological progression underlying microarray gene expression data.  ...  When applied to a cell cycle time series microarray dataset, SPD was not provided any prior knowledge of samples' time order or of which genes are cell-cycle regulated, yet SPD recovered the correct time  ...  Results SPD recovers temporal information of cell cycle time series data Microarray time series data of the cell cycle were used to evaluate the performance of SPD.  ... 
doi:10.1371/journal.pcbi.1001123 pmid:21533210 pmcid:PMC3077357 fatcat:joynlv44dvfiddimaiu5akus74

Unraveling complex temporal associations in cellular systems across multiple time-series microarray datasets

Wenyuan Li, Min Xu, Xianghong Jasmine Zhou
2010 Journal of Biomedical Informatics  
We introduce the novel concept of a "frequent temporal association pattern" (FTAP): a set of genes simultaneously exhibit complex temporal expression patterns recurrently across multiple microarray datasets  ...  Second, we look for a set of genes that simultaneously exhibit their respective trends recurrently in multiple datasets. We applied this algorithm to 18 yeast time-series microarray datasets.  ...  We applied the method to 18 yeast microarray time-series datasets, and discovered a large number of FTAPs.  ... 
doi:10.1016/j.jbi.2009.12.006 pmid:20083231 fatcat:5za26rv6kjf4jcaja2w56yk6ai

Identification of temporal association rules from time-series microarray data sets

Hojung Nam, KiYoung Lee, Doheon Lee
2009 BMC Bioinformatics  
The proposed TARM method is tested with Saccharomyces cerevisiae cell cycle time-series microarray gene expression data set.  ...  One of the most challenging problems in mining gene expression data is to identify how the expression of any particular gene affects the expression of other genes.  ...  From the result of the ARM method, it is possible to discover interactions between correlated expressions of genes in microarray experiments.  ... 
doi:10.1186/1471-2105-10-s3-s6 pmid:19344482 pmcid:PMC2665054 fatcat:szjavispcfa47ltlso2eg4wlhi

Automated Discovery of Functional Generality of Human Gene Expression Programs

Georg K. Gerber, Robin D. Dowell, Tommi S. Jaakkola, David K. Gifford
2007 PLoS Computational Biology  
We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules.  ...  , uncertainty in the numbers of programs and sample populations, and temporal behavior.  ...  GKG and RDD were supported by US National Institutes of Health (NIH) grant 2R01 HG002668-04A1; GKG was also supported by the Harvard/MIT HST MEMP fellowship, and RDD was also supported by NIH Grant DK076284  ... 
doi:10.1371/journal.pcbi.0030148 pmid:17696603 pmcid:PMC1941755 fatcat:zgnqjhteazbnrdfvlhgm3kfhra

Automated discovery of functional generality of human gene expression programs

Georg Kurt Gerber, Robin D Dowell, Tommi Jaakkola, David Gifford
2005 PLoS Computational Biology  
We applied GeneProgram to a compendium of 62 short time-series gene expression datasets exploring the responses of human cells to infectious agents and immune-modulating molecules.  ...  , uncertainty in the numbers of programs and sample populations, and temporal behavior.  ...  GKG and RDD were supported by US National Institutes of Health (NIH) grant 2R01 HG002668-04A1; GKG was also supported by the Harvard/MIT HST MEMP fellowship, and RDD was also supported by NIH Grant DK076284  ... 
doi:10.1371/journal.pcbi.0030148.eor fatcat:o3lfd5oe5jg2doqa23mxmpw5jy

Empirical comparison of tests for differential expression on time-series microarray experiments

Ernest A. Fischer, Michael A. Friedman, Mia K. Markey
2007 Genomics  
Methods for identifying differentially expressed genes were compared on time-series microarray data simulated from artificial gene networks.  ...  Based on the Boldrick et al. data, ANOVA is best suited to detect changes in temporal data, while GSVD and empirical Bayes effectively detect individual spikes or overall shifts, respectively.  ...  Acknowledgments We thank Zack Mahdavi and Chris Kite for technical support and Dan Bozinov for helpful discussions on microarray normalization.  ... 
doi:10.1016/j.ygeno.2006.10.008 pmid:17188839 fatcat:hpnsw6o4r5evxeinvqiiz5xd6m

Developmental Changes in the Transcriptome of Human Cerebral Cortex Tissue: Long Noncoding RNA Transcripts

Leonard Lipovich, Adi L. Tarca, Juan Cai, Hui Jia, Harry T. Chugani, Kirstin N. Sterner, Lawrence I. Grossman, Monica Uddin, Patrick R. Hof, Chet C. Sherwood, Christopher W. Kuzawa, Morris Goodman (+1 others)
2013 Cerebral Cortex  
Our analysis identified 8 lncRNA genes with distinct developmental expression patterns.  ...  Recently, a new large class of mammalian genes, encoding nonmessenger, long nonprotein-coding ribonucleic acid (lncRNA) molecules has been discovered.  ...  Conflict of Interest: None declared.  ... 
doi:10.1093/cercor/bhs414 pmid:23377288 fatcat:mv52k2nbanf53jo2okpsy4jowe
« Previous Showing results 1 — 15 out of 8,466 results