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Full-Text Articles in Medicine and Health Sciences
Prognosis Of Stage Ii Colon Cancer By Non-Neoplastic Mucosa Gene Expresssion Profiling, Alain Barrier, Sandrine Dudoit, Et Al.
Prognosis Of Stage Ii Colon Cancer By Non-Neoplastic Mucosa Gene Expresssion Profiling, Alain Barrier, Sandrine Dudoit, Et Al.
U.C. Berkeley Division of Biostatistics Working Paper Series
Aims. This study assessed the possibility to build a prognosis predictor, based on non-neoplastic mucosa microarray gene expression measures, in stage II colon cancer patients. Materials and Methods. Non-neoplastic colonic mucosa mRNA samples from 24 patients (10 with a metachronous metastasis, 14 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. The k-nearest neighbor method was used for prognosis prediction using microarray gene expression measures. Leave-one-out cross-validation was used to select the number of neighbors and number of informative genes to include in the predictor. Based on this information, a prognosis predictor was proposed and its accuracy estimated by …
Colon Cancer Prognosis Prediction By Gene Expression Profiling, Alain Barrier, Sandrine Dudoit, Et Al.
Colon Cancer Prognosis Prediction By Gene Expression Profiling, Alain Barrier, Sandrine Dudoit, Et Al.
U.C. Berkeley Division of Biostatistics Working Paper Series
Aims. This study assessed the possibility to build a prognosis predictor, based on microarray gene expression measures, in stage II and III colon cancer patients. Materials and Methods. Tumour (T) and non-neoplastic mucosa (NM) mRNA samples from 18 patients (9 with a recurrence, 9 with no recurrence) were profiled using the Affymetrix HGU133A GeneChip. The k-nearest neighbour method was used for prognosis prediction using T and NM gene expression measures. Six-fold cross-validation was applied to select the number of neighbours and the number of informative genes to include in the predictors. Based on this information, one T-based and one NM-based …
Data Adaptive Estimation Of The Treatment Specific Mean, Yue Wang, Oliver Bembom, Mark J. Van Der Laan
Data Adaptive Estimation Of The Treatment Specific Mean, Yue Wang, Oliver Bembom, Mark J. Van Der Laan
U.C. Berkeley Division of Biostatistics Working Paper Series
An important problem in epidemiology and medical research is the estimation of the causal effect of a treatment action at a single point in time on the mean of an outcome, possibly within strata of the target population defined by a subset of the baseline covariates. Current approaches to this problem are based on marginal structural models, i.e., parametric models for the marginal distribution of counterfactural outcomes as a function of treatment and effect modifiers. The various estimators developed in this context furthermore each depend on a high-dimensional nuisance parameter whose estimation currently also relies on parametric models. Since misspecification …
Multiple Testing Methods For Chip-Chip High Density Oligonucleotide Array Data, Sunduz Keles, Mark J. Van Der Laan, Sandrine Dudoit, Simon E. Cawley
Multiple Testing Methods For Chip-Chip High Density Oligonucleotide Array Data, Sunduz Keles, Mark J. Van Der Laan, Sandrine Dudoit, Simon E. Cawley
U.C. Berkeley Division of Biostatistics Working Paper Series
Cawley et al. (2004) have recently mapped the locations of binding sites for three transcription factors along human chromosomes 21 and 22 using ChIP-Chip experiments. ChIP-Chip experiments are a new approach to the genome-wide identification of transcription factor binding sites and consist of chromatin (Ch) immunoprecipitation (IP) of transcription factor-bound genomic DNA followed by high density oligonucleotide hybridization (Chip) of the IP-enriched DNA. We investigate the ChIP-Chip data structure and propose methods for inferring the location of transcription factor binding sites from these data. The proposed methods involve testing for each probe whether it is part of a bound sequence …