Loss-Based Estimation with Cross-Validation: Applications to Microarray Data Analysis and Motif Finding

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using this (or possibly another) loss function. Cross-validation is applied to select an optimal estimator among the candidates and to assess the overall performance of the resulting estimator. This general estimation framework encompasses a number of problems which have traditionally been treated separately in the statistical literature, including multivariate outcome prediction and density estimation based on either uncensored or censored data. This article provides an overview of the methodology and describes its application to two problems in genomic data analysis: the prediction of biological and clinical outcomes (possibly censored) using microarray gene expression measures and the identification of regulatory motifs (i.e., transcription factor binding sites) in DNA sequences

Attitudes of Singaporean consumers towards foreign language films

This study examines local consumer attitudes towards foreign language films (FLFs) vis-à-vis mainstream films. It considers country-of-origin (COO) effects on consumer attitudes and behaviour, including consumers’ reliance on the COO cue in the evaluation of FLFs

Loss-Based Estimation with Cross-Validation: Applications to Microarray Data Analysis and Motif Finding

Current statistical inference problems in genomic data analysis involve parameter estimation for high-dimensional multivariate distributions, with typically unknown and intricate correlation patterns among variables. Addressing these inference questions satisfactorily requires: (i) an intensive and thorough search of the parameter space to generate good candidate estimators, (ii) an approach for selecting an optimal estimator among these candidates, and (iii) a method for reliably assessing the performance of the resulting estimator. We propose a unified loss-based methodology for estimator construction, selection, and performance assessment with cross-validation. In this approach, the parameter of interest is defined as the risk minimizer for a suitable loss function and candidate estimators are generated using this (or possibly another) loss function. Cross-validation is applied to select an optimal estimator among the candidates and to assess the overall performance of the resulting estimator. This general estimation framework encompasses a number of problems which have traditionally been treated separately in the statistical literature, including multivariate outcome prediction and density estimation based on either uncensored or censored data. This article provides an overview of the methodology and describes its application to two problems in genomic data analysis: the prediction of biological and clinical outcomes (possibly censored) using microarray gene expression measures and the identification of regulatory motifs (i.e., transcription factor binding sites) in DNA sequences.Censored data, classification, comparative genomic hybridization, cross-validation, density estimation, estimation, loss function, microarray, model selection, motif finding, multivariate outcome, prediction, regression trees, risk, sequence analysis, survival analysis, variable selection,

Re-Examining of Moffitt’s Theory of Delinquency through Agent Based Modeling

Moffitt’s theory of delinquency suggests that at-risk youths can be divided into two groups, the adolescence- limited group and the life-course-persistent group, predetermined at a young age, and social interactions between these two groups become important during the adolescent years. We built an agent-based model based on the microscopic interactions Moffitt described: (i) a maturity gap that dictates (ii) the cost and reward of antisocial behavior, and (iii) agents imitating the antisocial behaviors of others more successful than themselves, to find indeed the two groups emerging in our simulations. Moreover, through an intervention simulation where we moved selected agents from one social network to another, we also found that the social network plays an important role in shaping the life course outcome.Published versio

Performance of the HIV Blot 2.2, INNO-LIA HIV I/II Score, and Geenius HIV 1/2 Confirmatory Assay for use in HIV confirmation.

In view of recent revised recommendations for human immunodeficiency virus (HIV) confirmatory testing, the performance of 3 HIV confirmatory assays was compared. Using the HIV Blot 2.2 (MP-WB), the INNO-LIA HIV I/II Score (INNO), and the Geenius HIV 1/2 Confirmatory Assay (Geenius), we tested 199 HIV-1 positive, 161 HIV negative, 65 HIV western blot indeterminate, 26 HIV seroconversion, 34 early HIV infection and 4 HIV-2 positive archived specimens. We show that all 3 assays had comparable test sensitivity in the detection of HIV-1 positive cases. However, less non-specific reactivity was observed with the INNO and Geenius assays, where both of them were able to resolve MP-WB indeterminate cases. When early HIV cases were considered, INNO and Geenius were more likely to confirm an early-stage infection as positive. Nevertheless, overall poor sensitivity (25.5% - 44.7%) of these assays for the detection of early cases was observed, likely because these cases had very low or non-detectable levels of HIV antibodies. Hence, further testing by a nucleic acid test or a p24 antigen test of specimens reactive on screening with a fourth generation Ag/Ab assay that are negative on confirmatory testing for HIV-specific antibody, may be useful. In conclusion, INNO and Geenius had comparable test performance, although the ease of use and shorter assay time for Geenius may make it the preferred choice for laboratories. In that regard, of note is our observation of non-specific reactivity of lipaemic specimens to the HIV-2 gp140 band in the Geenius assay, which should prompt caution when interpreting results of such specimens

Test results using the MP Biomedicals HIV Blot 2.2 assay, Fujirebio INNO-LIA HIV I/II Score, and Bio-Rad Geenius HIV 1/2 Confirmatory Assay¹.

<p>Test results using the MP Biomedicals HIV Blot 2.2 assay, Fujirebio INNO-LIA HIV I/II Score, and Bio-Rad Geenius HIV 1/2 Confirmatory Assay<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0199502#t002fn001" target="_blank">¹</a>.</p

Performance of the Fujirebio INNO-LIA HIV I/II Score and Bio-Rad Geenius HIV 1/2 Confirmatory Assay.

<p>Performance of the Fujirebio INNO-LIA HIV I/II Score and Bio-Rad Geenius HIV 1/2 Confirmatory Assay.</p

Breakdown of results for early HIV cases.

<p>Breakdown of results for early HIV cases.</p