Pre-diagnosis thyroid hormone dysfunction is associated with cancer mortality.

Research on the association between thyroid hormone levels and cancer mortality remains limited and inconclusive. We determined the relation of thyroid stimulating hormone (TSH), free T4 (FT4), and free T3 (FT3) levels with mortality in overall cancer and specific tumor types. Thyroid hormone levels 1-5 years prior to cancer diagnosis, as well as multiple clinical and demographic parameters, were retrospectively collected for 10,325 Israeli cancer patients, diagnosed between 2000 and 2016.

Opposing effects of thyroid hormones on cancer risk: a population-based study.

Objective: The association between dysregulated thyroid hormone function and cancer risk is inconclusive, especially among different age groups and uncommon malignancies. We sought to determine the relation of TSH and free T4 levels with overall cancer risk as well as risk of specific cancer types.

Design And Methods: Data on thyroid hormone profile was collected from 375 635 Israeli patients with no prior history of cancer.

Getting more out of meta-analyses: a new approach to meta-analysis in light of unexplained heterogeneity.

Background And Objectives: Meta-analyses sometimes summarize results in the presence of substantial unexplained between-study heterogeneity. As GRADE criteria highlight, unexplained heterogeneity reduces certainty in the evidence, resulting in limited confidence in average effect estimates. The aim of this paper is to provide a new clinically useful approach to estimating an intervention effect in light of unexplained heterogeneity.

Extracting replicable associations across multiple studies: Empirical Bayes algorithms for controlling the false discovery rate.

In almost every field in genomics, large-scale biomedical datasets are used to report associations. Extracting associations that recur across multiple studies while controlling the false discovery rate is a fundamental challenge. Here, we propose a new method to allow joint analysis of multiple studies.

A hierarchical Bayesian model for flexible module discovery in three-way time-series data.

Motivation: Detecting modules of co-ordinated activity is fundamental in the analysis of large biological studies. For two-dimensional data (e.g.

Is this the right normalization? A diagnostic tool for ChIP-seq normalization.

Background: Chip-seq experiments are becoming a standard approach for genome-wide profiling protein-DNA interactions, such as detecting transcription factor binding sites, histone modification marks and RNA Polymerase II occupancy. However, when comparing a ChIP sample versus a control sample, such as Input DNA, normalization procedures have to be applied in order to remove experimental source of biases. Despite the substantial impact that the choice of the normalization method can have on the results of a ChIP-seq data analysis, their assessment is not fully explored in the literature.

repfdr: a tool for replicability analysis for genome-wide association studies.

Motivation: Identification of single nucleotide polymorphisms that are associated with a phenotype in more than one study is of great scientific interest in the genome-wide association studies (GWAS) research. The empirical Bayes approach for discovering whether results have been replicated across studies was shown to be a reliable method, and close to optimal in terms of power.

Results: The R package repfdr provides a flexible implementation of the empirical Bayes approach for replicability analysis and meta-analysis, to be used when several studies examine the same set of null hypotheses.

The accuracy of Gram stain of respiratory specimens in excluding Staphylococcus aureus in ventilator-associated pneumonia.

Objective: To evaluate the Gram stain of deep tracheal aspirate as a tool to direct empiric antibiotic therapy, and more specifically as a tool to exclude the need for empiric antibiotic coverage against Staphylococcus aureus in ventilator-associated pneumonia (VAP).

Design: A prospective, single-center, observational, cohort study.

Setting: All wards at a community hospital.

Selective inference in complex research.

We explain the problem of selective inference in complex research using a recently published study: a replicability study of the associations in order to reveal and establish risk loci for type 2 diabetes. The false discovery rate approach to such problems will be reviewed, and we further address two problems: (i) setting confidence intervals on the size of the risk at the selected locations and (ii) selecting the replicable results.

Data mining in a behavioral test detects early symptoms in a model of amyotrophic lateral sclerosis.

"What's wrong with my genetically engineered animal?" is a common yet often difficult to answer question in behavioral phenotyping. We present here a method termed Pattern Array for mining movement patterns and isolating those that best capture an effect of a genetic manipulation. We demonstrate the method by searching for early motor symptoms in the open-field behavior of SOD1 mutant rats, an animal model of amyotrophic lateral sclerosis.

A data mining approach to in vivo classification of psychopharmacological drugs.

Data mining is a powerful bioinformatics strategy that has been successfully applied in vitro to screen for gene-expression profiles predicting toxicological or carcinogenic response ('class predictors'). In this report we used a data mining algorithm named Pattern Array (PA) in vivo to analyze mouse open-field behavior and characterize the psychopharmacological effects of three drug classes--psychomotor stimulant, opioid, and psychotomimetic. PA represents rodent movement with approximately 100,000 complex patterns, defined as multiple combinations of several ethologically relevant variables, and mines them for those that maximize any effect of interest, such as the difference between drug classes.

Computational characterization of B-cell epitopes.

Characterizing B-cell epitopes is a fundamental step for understanding the immunological basis of bio-recognition. To date, epitope analyses have either been based on limited structural data, or sequence data alone. In this study, our null hypothesis was that the surface of the antigen is homogeneously antigenic.

Associating quantitative behavioral traits with gene expression in the brain: searching for diamonds in the hay.

Unlabelled: Gene expression and phenotypic functionality can best be associated when they are measured quantitatively within the same experiment. The analysis of such a complex experiment is presented, searching for associations between measures of exploratory behavior in mice and gene expression in brain regions. The analysis of such experiments raises several methodological problems.

Genomic analysis of COP9 signalosome function in Drosophila melanogaster reveals a role in temporal regulation of gene expression.

The COP9 signalosome (CSN), an eight-subunit protein complex, is conserved in all higher eukaryotes. CSN intersects the ubiquitin-proteasome pathway, modulating signaling pathways controlling various aspects of development. We are using Drosophila as a model system to elucidate the function of this important complex.

SKY analysis of childhood neural tumors and cell lines demonstrates a susceptibility of aberrant chromosomes to further rearrangements.

Malignant solid tumors are commonly characterized by a large number of complex structural and numerical chromosomal alterations, which often reflect the level of genomic instability and can be associated with disease progression. The aim of this study was to evaluate whether chromosomes that harbor primary aberrations have a higher susceptibility to accumulate further alterations. We used spectral karyotyping (SKY), to compare the individual chromosomal instability of two chromosome types: chromosomes that have a primary aberration and chromosomes without an aberration, in 13 primary childhood neural tumors and seven cell lines.

Cluster-based analysis of FMRI data.

We propose a method for the statistical analysis of fMRI data that tests cluster units rather than voxel units for activation. The advantages of this analysis over previous ones are both conceptual and statistical. Recognizing that the fundamental units of interest are the spatially contiguous clusters of voxels that are activated together, we set out to approximate these cluster units from the data by a clustering algorithm especially tailored for fMRI data.

Quantitative trait Loci analysis using the false discovery rate.

False discovery rate control has become an essential tool in any study that has a very large multiplicity problem. False discovery rate-controlling procedures have also been found to be very effective in QTL analysis, ensuring reproducible results with few falsely discovered linkages and offering increased power to discover QTL, although their acceptance has been slower than in microarray analysis, for example. The reason is partly because the methodological aspects of applying the false discovery rate to QTL mapping are not well developed.

Prior exercise training improves the outcome of acute myocardial infarction in the rat. Heart structure, function, and gene expression.

Objectives: The aim of this research was to investigate the structural, functional, and molecular features of the remodeling heart in prior swim-trained infarcted rats.

Background: Physical exercise training is a known protective factor against cardiovascular morbidity and mortality. The structural and molecular aspects underlying this protection in the remodeling heart have not been investigated.

Identifying differentially expressed genes using false discovery rate controlling procedures.

Motivation: DNA microarrays have recently been used for the purpose of monitoring expression levels of thousands of genes simultaneously and identifying those genes that are differentially expressed. The probability that a false identification (type I error) is committed can increase sharply when the number of tested genes gets large. Correlation between the test statistics attributed to gene co-regulation and dependency in the measurement errors of the gene expression levels further complicates the problem.