A novel family of beta mixture models for the differential analysis of DNA methylation data: An application to prostate cancer.

Identifying differentially methylated cytosine-guanine dinucleotide (CpG) sites between benign and tumour samples can assist in understanding disease. However, differential analysis of bounded DNA methylation data often requires data transformation, reducing biological interpretability. To address this, a family of beta mixture models (BMMs) is proposed that (i) objectively infers methylation state thresholds and (ii) identifies differentially methylated CpG sites (DMCs) given untransformed, beta-valued methylation data.

Adaptive data collection for intraindividual studies affected by adherence.

Recently, the use of mobile technologies in ecological momentary assessments (EMAs) and interventions has made it easier to collect data suitable for intraindividual variability studies in the medical field. Nevertheless, especially when self-reports are used during the data collection process, there are difficulties in balancing data quality and the burden placed on the subject. In this paper, we address this problem for a specific EMA setting that aims to submit a demanding task to subjects at high/low values of a self-reported variable.

Model-based clustering for random hypergraphs.

A probabilistic model for random hypergraphs is introduced to represent unary, binary and higher order interactions among objects in real-world problems. This model is an extension of the latent class analysis model that introduces two clustering structures for hyperedges and captures variation in the size of hyperedges. An expectation maximization algorithm with minorization maximization steps is developed to perform parameter estimation.

Unobserved classes and extra variables in high-dimensional discriminant analysis.

In supervised classification problems, the test set may contain data points belonging to classes not observed in the learning phase. Moreover, the same units in the test data may be measured on a set of additional variables recorded at a subsequent stage with respect to when the learning sample was collected. In this situation, the classifier built in the learning phase needs to adapt to handle potential unknown classes and the extra dimensions.

Weighted stochastic block model.

We propose a weighted stochastic block model (WSBM) which extends the stochastic block model to the important case in which edges are weighted. We address the parameter estimation of the WSBM by use of maximum likelihood and variational approaches, and establish the consistency of these estimators. The problem of choosing the number of classes in a WSBM is addressed.

Robust variable selection in the framework of classification with label noise and outliers: Applications to spectroscopic data in agri-food.

Classification of high-dimensional spectroscopic data is a common task in analytical chemistry. Well-established procedures like support vector machines (SVMs) and partial least squares discriminant analysis (PLS-DA) are the most common methods for tackling this supervised learning problem. Nonetheless, interpretation of these models remains sometimes difficult, and solutions based on feature selection are often adopted as they lead to the automatic identification of the most informative wavelengths.

Integrating inflammatory serum biomarkers into a risk calculator for prostate cancer detection.

Improved prostate cancer detection methods would avoid over-diagnosis of clinically indolent disease informing appropriate treatment decisions. The aims of this study were to investigate the role of a panel of Inflammation biomarkers to inform the need for a biopsy to diagnose prostate cancer. Peripheral blood serum obtained from 436 men undergoing transrectal ultrasound guided biopsy were assessed for a panel of 18 inflammatory serum biomarkers in addition to Total and Free Prostate Specific Antigen (PSA).

Multiresolution Network Models.

Many existing statistical and machine learning tools for social network analysis focus on a single level of analysis. Methods designed for clustering optimize a global partition of the graph, whereas projection-based approaches (e.g.

Role of serum response factor expression in prostate cancer biochemical recurrence.

Background: Up to a third of prostate cancer patients fail curative treatment strategies such as surgery and radiation therapy in the form of biochemical recurrence (BCR) which can be predictive of poor outcome. Recent clinical trials have shown that men experiencing BCR might benefit from earlier intervention post-radical prostatectomy (RP). Therefore, there is an urgent need to identify earlier prognostic biomarkers which will guide clinicians in making accurate diagnosis and timely decisions on the next appropriate treatment.

Role Analysis in Networks using Mixtures of Exponential Random Graph Models.

A novel and flexible framework for investigating the roles of actors within a network is introduced. Particular interest is in roles as defined by local network connectivity patterns, identified using the ego-networks extracted from the network. A mixture of Exponential-family Random Graph Models is developed for these ego-networks in order to cluster the nodes into roles.

Evaluation of prediction models for the staging of prostate cancer.

Background: There are dilemmas associated with the diagnosis and prognosis of prostate cancer which has lead to over diagnosis and over treatment. Prediction tools have been developed to assist the treatment of the disease.

Methods: A retrospective review was performed of the Irish Prostate Cancer Research Consortium database and 603 patients were used in the study.

Glycosylation status of serum in inflammatory arthritis in response to anti-TNF treatment.

Objective: Glycosylation is the most common post-translational modification and is altered in disease. The typical glycosylation change in patients with inflammatory arthritis (IA) is a decrease in galactosylation levels on IgG. The aim of this study is to evaluate the effect of anti-TNF therapy on whole serum glycosylation from IA patients and determine whether these alterations in the glycome change upon treatment of the disease.

Greedy feature selection for glycan chromatography data with the generalized Dirichlet distribution.

Background: Glycoproteins are involved in a diverse range of biochemical and biological processes. Changes in protein glycosylation are believed to occur in many diseases, particularly during cancer initiation and progression. The identification of biomarkers for human disease states is becoming increasingly important, as early detection is key to improving survival and recovery rates.

Family-mediated exercise intervention (FAME): evaluation of a novel form of exercise delivery after stroke.

Background And Purpose: Additional exercise therapy has been shown to have a positive impact on function after acute stroke and research is now focusing on methods to increase the amount of therapy that is delivered. This randomized controlled trial examined the impact of additional family-mediated exercise (FAME) therapy on outcome after acute stroke.

Methods: Forty participants with acute stroke were randomly assigned to either a control group who received routine therapy with no formal input from their family members or a FAME group, who received routine therapy and additional lower limb FAME therapy for 8 weeks.

Novel glycan biomarkers for the detection of lung cancer.

Lung cancer has a poor prognosis and a 5-year survival rate of 15%. Therefore, early detection is vital. Diagnostic testing of serum for cancer-associated biomarkers is a noninvasive detection method.

Applying random forests to identify biomarker panels in serum 2D-DIGE data for the detection and staging of prostate cancer.

In recent years, Prostate Specific Antigen (PSA) testing is widespread and has been associated with deceased mortality rates; however, this testing has raised concerns of overdiagnosis and overtreatment. It is clear that additional biomarkers are required. To identify these biomarkers, we have undertaken proteomics and metabolomics expression profiles of serum samples from BPH, Gleason score 5 and 7 using two-dimensional difference in gel electrophoresis (2D-DIGE) and nuclear magnetic resonance spectroscopy (NMR).

Variable Selection and Updating In Model-Based Discriminant Analysis for High Dimensional Data with Food Authenticity Applications.

Food authenticity studies are concerned with determining if food samples have been correctly labelled or not. Discriminant analysis methods are an integral part of the methodology for food authentication. Motivated by food authenticity applications, a model-based discriminant analysis method that includes variable selection is presented.

Model-based clustering of microarray expression data via latent Gaussian mixture models.

Motivation: In recent years, work has been carried out on clustering gene expression microarray data. Some approaches are developed from an algorithmic viewpoint whereas others are developed via the application of mixture models. In this article, a family of eight mixture models which utilizes the factor analysis covariance structure is extended to 12 models and applied to gene expression microarray data.