1.1 --- a/grant.bib Tue Apr 21 03:36:06 2009 -0700 1.2 +++ b/grant.bib Tue Apr 21 18:53:40 2009 -0700 1.3 @@ -73,7 +73,7 @@ 1.4 publisher = {{ACM}}, 1.5 author = {Bayle Shanks}, 1.6 year = {2005}, 1.7 - keywords = {atom,client-side wiki,interoperability,interwiki,middleware,webdav,wiki,wikiclient,wikigateway,wikirpcinterface,wiki xmlrpc}, 1.8 + keywords = {atom,client-side wiki,interoperability,interwiki,middleware,webdav,wiki,wiki xmlrpc,wikiclient,wikigateway,wikirpcinterface}, 1.9 pages = {53--66} 1.10 }, 1.11 1.12 @@ -218,7 +218,7 @@ 1.13 booktitle = {Computational Systems Bioinformatics Conference, 2005. Workshops and Poster Abstracts. {IEEE}}, 1.14 author = {J. Carson and T. Ju and C. Thaller and M. Bello and I. Kakadiaris and J. Warren and G. Eichele and W. Chiu}, 1.15 year = {2005}, 1.16 - keywords = {atlas-based segmentation,automate robotic in situ hybridization image annotation,biological techniques,biological tissues,biology {computing,Brain,cell-cell} signaling,cell differentiation,cellular biophysics,cellular resolution,cluster analysis,data {mining,DNA} sequence database,functional genomics,gene expression pattern,genetics,image classification,image segmentation,mesh maps,pattern clustering,postnatal mouse brain,query interface,statistical analysis,tissue}, 1.17 + keywords = {atlas-based segmentation,automate robotic in situ hybridization image annotation,biological techniques,biological tissues,biology {computing,Brain,cell} differentiation,cell-cell signaling,cellular biophysics,cellular resolution,cluster analysis,data {mining,DNA} sequence database,functional genomics,gene expression pattern,genetics,image classification,image segmentation,mesh maps,pattern clustering,postnatal mouse brain,query interface,statistical analysis,tissue}, 1.18 pages = {358} 1.19 }, 1.20 1.21 @@ -469,4 +469,36 @@ 1.22 author = {Chris Adamson and Leigh Johnston and Terrie Inder and Sandra Rees and Iven Mareels and Gary Egan}, 1.23 year = {2005}, 1.24 pages = {294--301} 1.25 +}, 1.26 + 1.27 +@article{paciorek_computational_2007, 1.28 + title = {Computational techniques for spatial logistic regression with large data sets}, 1.29 + volume = {51}, 1.30 + issn = {0167-9473}, 1.31 + url = {http://www.sciencedirect.com/science/article/B6V8V-4MG6JWS-2/2/dfe5cd9c7ac7bc39d22ce45eebe303b8}, 1.32 + doi = {10.1016/j.csda.2006.11.008}, 1.33 + abstract = {In epidemiological research, outcomes are frequently non-normal, sample sizes may be large, and effect sizes are often small. To relate health outcomes to geographic risk factors, fast and powerful methods for fitting spatial models, particularly for non-normal data, are required. I focus on binary outcomes, with the risk surface a smooth function of space, but the development herein is relevant for non-normal data in general. I compare penalized likelihood {(PL)} models, including the penalized quasi-likelihood {(PQL)} approach, and Bayesian models based on fit, speed, and ease of implementation. 1.34 +A Bayesian model using a spectral basis {(SB)} representation of the spatial surface via the Fourier basis provides the best tradeoff of sensitivity and specificity in simulations, detecting real spatial features while limiting overfitting and being reasonably computationally efficient. One of the contributions of this work is further development of this underused representation. The {SB} model outperforms the {PL} methods, which are prone to overfitting, but is slower to fit and not as easily implemented. A Bayesian Markov random field model performs less well statistically than the {SB} model, but is very computationally efficient. We illustrate the methods on a real data set of cancer cases in Taiwan. 1.35 +The success of the {SB} with binary data and similar results with count data suggest that it may be generally useful in spatial models and more complicated hierarchical models.}, 1.36 + number = {8}, 1.37 + journal = {Computational Statistics \& Data Analysis}, 1.38 + author = {Christopher J. Paciorek}, 1.39 + month = may, 1.40 + year = {2007}, 1.41 + keywords = {Bayesian {statistics,Disease} {mapping,Fourier} {basis,Generalized} linear mixed {model,Geostatistics,Risk} {surface,Spatial} {statistics,Spectral} basis}, 1.42 + pages = {3631--3653} 1.43 +}, 1.44 + 1.45 +@article{hastie_gene_2000, 1.46 + title = {{'Gene} shaving' as a method for identifying distinct sets of genes with similar expression patterns}, 1.47 + volume = {1}, 1.48 + issn = {1465-6906}, 1.49 + url = {http://genomebiology.com/2000/1/2/research/0003/}, 1.50 + doi = {10.1186/gb-2000-1-2-research0003}, 1.51 + abstract = {{BACKGROUND:Large} gene expression studies, such as those conducted using {DNA} arrays, often provide millions of different pieces of data. To address the problem of analyzing such data, we describe a statistical method, which we have called 'gene shaving'. The method identifies subsets of genes with coherent expression patterns and large variation across conditions. Gene shaving differs from hierarchical clustering and other widely used methods for analyzing gene expression studies in that genes may belong to more than one cluster, and the clustering may be supervised by an outcome measure. The technique can be 'unsupervised', that is, the genes and samples are treated as unlabeled, or partially or fully supervised by using known properties of the genes or samples to assist in finding meaningful {groupings.RESULTS:We} illustrate the use of the gene shaving method to analyze gene expression measurements made on samples from patients with diffuse large B-cell lymphoma. The method identifies a small cluster of genes whose expression is highly predictive of {survival.CONCLUSIONS:The} gene shaving method is a potentially useful tool for exploration of gene expression data and identification of interesting clusters of genes worth further investigation.}, 1.52 + number = {2}, 1.53 + journal = {Genome Biology}, 1.54 + author = {Trevor Hastie and Robert Tibshirani and Michael Eisen and Ash Alizadeh and Ronald Levy and Louis Staudt and Wing Chan and David Botstein and Patrick Brown}, 1.55 + year = {2000}, 1.56 + pages = {research0003.1--research0003.21} 1.57 } 1.58 \ No newline at end of file