@article {685,
	title = {Analysis of tiling array expression studies with flexible designs in Bioconductor (waveTiling.},
	journal = {BMC Bioinformatics},
	volume = {13},
	year = {2012},
	month = {2012 Sep 14},
	pages = {234},
	abstract = {<p>ABSTRACT: BACKGROUND: Existing statistical methods for tiling array transcriptome data either focus on transcript discovery in one biological or experimental condition or on the detection of differential expression between two conditions. Increasingly often, however, biologists are interested in time-course studies, studies with more than two conditions or even multiple-factor studies. As these studies are currently analyzed with the traditional microarray analysis techniques, they do not exploit the genome-wide nature of tiling array data to its full potential. RESULTS: We present an R Bioconductor package, waveTiling, which implements a wavelet-based model for analyzing transcriptome data and extends it towards more complex experimental designs. With waveTiling the user is able to discover (1) group-wise expressed regions, (2) differentially expressed regions between any two groups in single-factor studies and in (3) multifactorial designs. Moreover, for time-course experiments it is also possible to detect (4) linear time effects and (5) a circadian rhythm of transcripts. By considering the expression values of the individual tiling probes as a function of genomic position, effect regions can be detected regardless of existing annotation. Three case studies with different experimental set-ups illustrate the use and the flexibility of the model-based transcriptome analysis. CONCLUSIONS: The waveTiling package provides the user with a convenient tool for the analysis of tiling array trancriptome data for a multitude of experimental set-ups. Regardless of the study design, the probe-wise analysis allows for the detection of transcriptional effects in both exonic, intronic and intergenic regions, without prior consultation of existing annotation.</p>},
	issn = {1471-2105},
	doi = {10.1186/1471-2105-13-234},
	author = {De Beuf, K. and Pipelers, Peter and Andriankaja, Megan and Thas, O. and Inz{\'e}, Dirk and Crainiceanu, C and Clement, Lieven}
}
@article {calSimex,
	title = {Assessment of bias in experimentally measured diffusion tensor imaging parameters using SIMEX},
	journal = {Accepted in Magnetic Resonance in Medicine},
	year = {2012},
	author = {Lauzon, C and Crainiceanu, C and Caffo, B and Landman, B}
}
@article {621,
	title = {Automatic Lesion Incidence Estimation and Detection in Multiple Sclerosis Using Multisequence Longitudinal MRI.},
	journal = {AJNR Am J Neuroradiol},
	year = {2012},
	month = {2012 Jul 5},
	abstract = {<p>BACKGROUND AND PURPOSE:Detecting incidence and enlargement of lesions is essential in monitoring the progression of MS. In clinical trials, lesion load is observed by manually segmenting and comparing serial MR images, which is time consuming, costly, and prone to inter- and intraobserver variability. Subtracting images from consecutive time points nulls stable lesions, leaving only new lesion activity. We propose SuBLIME, an automated method for segmenting incident lesion voxels.MATERIALS AND METHODS:We used logistic regression models incorporating multiple MR imaging sequences and subtraction images from consecutive longitudinal studies to estimate voxel-level probabilities of lesion incidence. We used T1-weighted, T2-weighted, FLAIR, and PD volumes from a total of 110 MR imaging studies from 10 subjects.RESULTS:To assess the performance of the model, we assigned 5 subjects to a training set and the remaining 5 to a validation set. With SuBLIME, lesion incidence is detected and delineated in the validation set with an AUC of 99\% (95\% CI [97\%, 100\%]) at the voxel level.CONCLUSIONS:This fully automated and computationally fast method allows sensitive and specific detection of lesion incidence that can be applied to large collections of images. Using the explicit form of the statistical model, SuBLIME can easily be adapted to cases when more or fewer imaging sequences are available.</p>},
	issn = {1936-959X},
	doi = {10.3174/ajnr.A3172},
	author = {Sweeney, E M and Shinohara, R and Shea, C D and Reich, D S and Crainiceanu, C}
}
@article {pmid21995019,
	title = {Assessment of bias for MRI diffusion tensor imaging using SIMEX},
	journal = {Med Image Comput Comput Assist Interv},
	volume = {14},
	number = {Pt 2},
	year = {2011},
	pages = {107{\textendash}115},
	author = {Lauzon, C and Asman, A. J. and Crainiceanu, C and Caffo, B and Landman, B}
}
@article {Crai:Domi:adju:2008,
	title = {Adjustment Uncertainty in Effect Estimation},
	journal = {Biometrika},
	volume = {95},
	number = {3},
	year = {2008},
	pages = {635{\textendash}651},
	keywords = {Air pollution, Bayesian model averaging},
	author = {Crainiceanu, C and Dominici, Francesca and Parmigiani, G.}
}
@article {Luo:Crai:Loui:Chat:anal:2008,
	title = {Analysis of Smoking Cessation Patterns Using a Stochastic Mixed-Effects Model With a Latent Cured State},
	journal = {Journal of the American Statistical Association},
	volume = {103},
	number = {483},
	year = {2008},
	pages = {1002{\textendash}1013},
	keywords = {cure model, Mixed-effects model, Recurrent events, Smoking cessation, stochastic transition model},
	author = {Luo, Sheng and Crainiceanu, C and Louis, Thomas A. and Chatterjee, Nilanjan}
}
