Newborn hearing screening: A retrospective analysis of hit and false positive rates in the NICU and how they are influenced by risk factors for hearing loss in a suburban birthing hospital

The purpose of this study was to evaluate the effectiveness of a hearing screening program, particularly focusing on hit and false positive rates in the NICU and WBN at a top-rated birthing hospital in Saint Louis, MO. Additionally, the study examined how these rates may be influenced by risk factors for hearing loss.

Research practices that can prevent an inflation of false-positive rates

Recent studies have indicated that research practices in psychology may be susceptible to factors that increase false-positive rates, raising concerns about the possible prevalence of false-positive findings. The present article discusses several practices that may run counter to the inflation of false-positive rates. Taking these practices into account would lead to a more balanced view on the false-positive issue. Specifically, we argue that an inflation of false-positive rates would diminish, sometimes to a substantial degree, when researchers (a) have explicit a priori theoretical hypotheses, (b) include multiple replication studies in a single paper, and (c) collect additional data based on observed results. We report findings from simulation studies and statistical evidence that support these arguments. Being aware of these preventive factors allows researchers not to overestimate the pervasiveness of false-positives in psychology and to gauge the susceptibility of a paper to possible false-positives in practical and fair ways.

SURVIVAL PREDICTION FOR BRAIN TUMOR PATIENTS USING GENE EXPRESSION DATA

Brain tumor is one of the most aggressive types of cancer in humans, with an estimated median survival time of 12 months and only 4% of the patients surviving more than 5 years after disease diagnosis. Until recently, brain tumor prognosis has been based only on clinical information such as tumor grade and patient age, but there are reports indicating that molecular profiling of gliomas can reveal subgroups of patients with distinct survival rates. We hypothesize that coupling molecular profiling of brain tumors with clinical information might improve predictions of patient survival time and, consequently, better guide future treatment decisions. In order to evaluate this hypothesis, the general goal of this research is to build models for survival prediction of glioma patients using DNA molecular profiles (U133 Affymetrix gene expression microarrays) along with clinical information. First, a predictive Random Forest model is built for binary outcomes (i.e. short vs. long-term survival) and a small subset of genes whose expression values can be used to predict survival time is selected. Following, a new statistical methodology is developed for predicting time-to-death outcomes using Bayesian ensemble trees. Due to a large heterogeneity observed within prognostic classes obtained by the Random Forest model, prediction can be improved by relating time-to-death with gene expression profile directly. We propose a Bayesian ensemble model for survival prediction which is appropriate for high-dimensional data such as gene expression data. Our approach is based on the ensemble "sum-of-trees" model which is flexible to incorporate additive and interaction effects between genes. We specify a fully Bayesian hierarchical approach and illustrate our methodology for the CPH, Weibull, and AFT survival models. We overcome the lack of conjugacy using a latent variable formulation to model the covariate effects which decreases computation time for model fitting. Also, our proposed models provides a model-free way to select important predictive prognostic markers based on controlling false discovery rates. We compare the performance of our methods with baseline reference survival methods and apply our methodology to an unpublished data set of brain tumor survival times and gene expression data, selecting genes potentially related to the development of the disease under study. A closing discussion compares results obtained by Random Forest and Bayesian ensemble methods under the biological/clinical perspectives and highlights the statistical advantages and disadvantages of the new methodology in the context of DNA microarray data analysis.

NETWORK TOPOLOGY IN HUMAN PROTEIN INTERACTION DATA PREDICTS FUNCTIONAL ASSOCIATION

High-throughput assays, such as yeast two-hybrid system, have generated a huge amount of protein-protein interaction (PPI) data in the past decade. This tremendously increases the need for developing reliable methods to systematically and automatically suggest protein functions and relationships between them. With the available PPI data, it is now possible to study the functions and relationships in the context of a large-scale network. To data, several network-based schemes have been provided to effectively annotate protein functions on a large scale. However, due to those inherent noises in high-throughput data generation, new methods and algorithms should be developed to increase the reliability of functional annotations. Previous work in a yeast PPI network (Samanta and Liang, 2003) has shown that the local connection topology, particularly for two proteins sharing an unusually large number of neighbors, can predict functional associations between proteins, and hence suggest their functions. One advantage of the work is that their algorithm is not sensitive to noises (false positives) in high-throughput PPI data. In this study, we improved their prediction scheme by developing a new algorithm and new methods which we applied on a human PPI network to make a genome-wide functional inference. We used the new algorithm to measure and reduce the influence of hub proteins on detecting functionally associated proteins. We used the annotations of the Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) as independent and unbiased benchmarks to evaluate our algorithms and methods within the human PPI network. We showed that, compared with the previous work from Samanta and Liang, our algorithm and methods developed in this study improved the overall quality of functional inferences for human proteins. By applying the algorithms to the human PPI network, we obtained 4,233 significant functional associations among 1,754 proteins. Further comparisons of their KEGG and GO annotations allowed us to assign 466 KEGG pathway annotations to 274 proteins and 123 GO annotations to 114 proteins with estimated false discovery rates of <21% for KEGG and <30% for GO. We clustered 1,729 proteins by their functional associations and made pathway analysis to identify several subclusters that are highly enriched in certain signaling pathways. Particularly, we performed a detailed analysis on a subcluster enriched in the transforming growth factor β signaling pathway (P<10-50) which is important in cell proliferation and tumorigenesis. Analysis of another four subclusters also suggested potential new players in six signaling pathways worthy of further experimental investigations. Our study gives clear insight into the common neighbor-based prediction scheme and provides a reliable method for large-scale functional annotations in this post-genomic era.

The information bottleneck method for genome-wide association studies

In population studies, most current methods focus on identifying one outcome-related SNP at a time by testing for differences of genotype frequencies between disease and healthy groups or among different population groups. However, testing a great number of SNPs simultaneously has a problem of multiple testing and will give false-positive results. Although, this problem can be effectively dealt with through several approaches such as Bonferroni correction, permutation testing and false discovery rates, patterns of the joint effects by several genes, each with weak effect, might not be able to be determined. With the availability of high-throughput genotyping technology, searching for multiple scattered SNPs over the whole genome and modeling their joint effect on the target variable has become possible. Exhaustive search of all SNP subsets is computationally infeasible for millions of SNPs in a genome-wide study. Several effective feature selection methods combined with classification functions have been proposed to search for an optimal SNP subset among big data sets where the number of feature SNPs far exceeds the number of observations. ^ In this study, we take two steps to achieve the goal. First we selected 1000 SNPs through an effective filter method and then we performed a feature selection wrapped around a classifier to identify an optimal SNP subset for predicting disease. And also we developed a novel classification method-sequential information bottleneck method wrapped inside different search algorithms to identify an optimal subset of SNPs for classifying the outcome variable. This new method was compared with the classical linear discriminant analysis in terms of classification performance. Finally, we performed chi-square test to look at the relationship between each SNP and disease from another point of view. ^ In general, our results show that filtering features using harmononic mean of sensitivity and specificity(HMSS) through linear discriminant analysis (LDA) is better than using LDA training accuracy or mutual information in our study. Our results also demonstrate that exhaustive search of a small subset with one SNP, two SNPs or 3 SNP subset based on best 100 composite 2-SNPs can find an optimal subset and further inclusion of more SNPs through heuristic algorithm doesn't always increase the performance of SNP subsets. Although sequential forward floating selection can be applied to prevent from the nesting effect of forward selection, it does not always out-perform the latter due to overfitting from observing more complex subset states. ^ Our results also indicate that HMSS as a criterion to evaluate the classification ability of a function can be used in imbalanced data without modifying the original dataset as against classification accuracy. Our four studies suggest that Sequential Information Bottleneck(sIB), a new unsupervised technique, can be adopted to predict the outcome and its ability to detect the target status is superior to the traditional LDA in the study. ^ From our results we can see that the best test probability-HMSS for predicting CVD, stroke,CAD and psoriasis through sIB is 0.59406, 0.641815, 0.645315 and 0.678658, respectively. In terms of group prediction accuracy, the highest test accuracy of sIB for diagnosing a normal status among controls can reach 0.708999, 0.863216, 0.639918 and 0.850275 respectively in the four studies if the test accuracy among cases is required to be not less than 0.4. On the other hand, the highest test accuracy of sIB for diagnosing a disease among cases can reach 0.748644, 0.789916, 0.705701 and 0.749436 respectively in the four studies if the test accuracy among controls is required to be at least 0.4. ^ A further genome-wide association study through Chi square test shows that there are no significant SNPs detected at the cut-off level 9.09451E-08 in the Framingham heart study of CVD. Study results in WTCCC can only detect two significant SNPs that are associated with CAD. In the genome-wide study of psoriasis most of top 20 SNP markers with impressive classification accuracy are also significantly associated with the disease through chi-square test at the cut-off value 1.11E-07. ^ Although our classification methods can achieve high accuracy in the study, complete descriptions of those classification results(95% confidence interval or statistical test of differences) require more cost-effective methods or efficient computing system, both of which can't be accomplished currently in our genome-wide study. We should also note that the purpose of this study is to identify subsets of SNPs with high prediction ability and those SNPs with good discriminant power are not necessary to be causal markers for the disease.^

Optimized hash for network path encoding with minimized false positives

The Bloom filter is a space efficient randomized data structure for representing a set and supporting membership queries. Bloom filters intrinsically allow false positives. However, the space savings they offer outweigh the disadvantage if the false positive rates are kept sufficiently low. Inspired by the recent application of the Bloom filter in a novel multicast forwarding fabric, this paper proposes a variant of the Bloom filter, the optihash. The optihash introduces an optimization for the false positive rate at the stage of Bloom filter formation using the same amount of space at the cost of slightly more processing than the classic Bloom filter. Often Bloom filters are used in situations where a fixed amount of space is a primary constraint. We present the optihash as a good alternative to Bloom filters since the amount of space is the same and the improvements in false positives can justify the additional processing. Specifically, we show via simulations and numerical analysis that using the optihash the false positives occurrences can be reduced and controlled at a cost of small additional processing. The simulations are carried out for in-packet forwarding. In this framework, the Bloom filter is used as a compact link/route identifier and it is placed in the packet header to encode the route. At each node, the Bloom filter is queried for membership in order to make forwarding decisions. A false positive in the forwarding decision is translated into packets forwarded along an unintended outgoing link. By using the optihash, false positives can be reduced. The optimization processing is carried out in an entity termed the Topology Manger which is part of the control plane of the multicast forwarding fabric. This processing is only carried out on a per-session basis, not for every packet. The aim of this paper is to present the optihash and evaluate its false positive performances via simulations in order to measure the influence of different parameters on the false positive rate. The false positive rate for the optihash is then compared with the false positive probability of the classic Bloom filter.

False -positives and false-negatives in tests for trend

False-positive and false-negative values were calculated for five different designs of the trend test and it was demonstrated that a design suggested by Portier and Hoel in 1984 for a different problem produced the lowest false-positive and false-negative rates when applied to historical spontaneous tumor rate data for Fischer Rats. ^

Using mixture models to detect differentially expressed genes

An important and common problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. As this problem concerns the selection of significant genes from a large pool of candidate genes, it needs to be carried out within the framework of multiple hypothesis testing. In this paper, we focus on the use of mixture models to handle the multiplicity issue. With this approach, a measure of the local false discovery rate is provided for each gene, and it can be implemented so that the implied global false discovery rate is bounded as with the Benjamini-Hochberg methodology based on tail areas. The latter procedure is too conservative, unless it is modified according to the prior probability that a gene is not differentially expressed. An attractive feature of the mixture model approach is that it provides a framework for the estimation of this probability and its subsequent use in forming a decision rule. The rule can also be formed to take the false negative rate into account.

A simple implementation of a normal mixture approach to differential gene expression in multiclass microarrays

Motivation: An important problem in microarray experiments is the detection of genes that are differentially expressed in a given number of classes. We provide a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. Current methods of implementing this approach either have some limitations due to the minimal assumptions made or with more specific assumptions are computationally intensive. Results: By converting to a z-score the value of the test statistic used to test the significance of each gene, we propose a simple two-component normal mixture that models adequately the distribution of this score. The usefulness of our approach is demonstrated on three real datasets.

True and false confessions to an intentional act: A novel experimental paradigm

Due to the powerful nature of confession evidence, it is imperative that we investigate the factors that affect the likelihood of obtaining true and false confessions. Previous research has been conducted with a paradigm limited to the study of false confessions to an act of negligence, thereby limiting the generalizability of the findings. The first goal of the current study was to introduce a novel paradigm involving a more serious, intentional act that can be used in the study of both true and false confessions. The second goal was to explore the effects of two police interrogation tactics, minimization and an offer of leniency, on true and false confession rates. ^ Three hundred and thirty-four undergraduates at a large southeastern university were recruited to participate in a study on problem-solving and decision-making. During the course of the laboratory experiment, participants were induced to intentionally break or not break an experimental rule, an act that was characterized as “cheating.” All participants (i.e., both innocent and guilty) were later accused of the act and interrogated. For half of the participants, the interrogator used minimization tactics, which involved downplaying the seriousness of the offense, expressing sympathy, and providing face-saving excuses, in order to encourage the participant to confess. An offer of leniency was also manipulated in which half the participants were offered a “deal” that involved the option of confessing and accepting a known punishment or not confessing and facing the threat of harsher punishment. Results indicated that guilty persons were more likely to confess than innocent persons, and that the use of minimization and an explicit offer of leniency increased both the true and false confession rates. Furthermore, a cumulative effect of techniques was observed, such that the diagnosticity of the interrogation (the ratio of true confessions to false confessions) decreased as the number of techniques used increased. Taken together, the results suggest that caution should be used when implementing these techniques in the interrogation room. ^

Fine needle aspiration biopsy in the oral cavity and head and neck region

The objective of the current study was to evaluate the sensitivity, specificity and accuracy of fine needle aspiration biopsy (FNAB) of submucous nodules from the oral cavity and head and neck region as an auxiliary diagnostic tool. Fifty patients with nodule lesions in the oral cavity and the head and neck region were selected. All of them were submitted to FNAB and to either incisional or excisional biopsy. The diagnoses from the FNABs were compared with the biopsy diagnosis as the gold standard. All the cases of FNAB were analyzed by a single oral pathologist prior to the biopsy diagnosis. The results showed that the sensitivity of FNAB was 75%, its specificity was 96% and its accuracy was 58.8%. The false positive and false negative rates were 6.7% and 13.3%, respectively. The positive predictive value was 86% and the negative predictive value was 93%. The inconclusive rate was 16/50. FNAB displayed a high success rate for identifying both malignant and benign lesions, but a low accuracy for making a final diagnosis.

Construct and Compare Gene Coexpression Networks with DAPfinder and DAPview

Background: DAPfinder and DAPview are novel BRB-ArrayTools plug-ins to construct gene coexpression networks and identify significant differences in pairwise gene-gene coexpression between two phenotypes. Results: Each significant difference in gene-gene association represents a Differentially Associated Pair (DAP). Our tools include several choices of filtering methods, gene-gene association metrics, statistical testing methods and multiple comparison adjustments. Network results are easily displayed in Cytoscape. Analyses of glioma experiments and microarray simulations demonstrate the utility of these tools. Conclusions: DAPfinder is a new friendly-user tool for reconstruction and comparison of biological networks.