Using neural biomarkers to personalize dosing of vagus nerve stimulation.

Background: Vagus nerve stimulation (VNS) is an established therapy for treating a variety of chronic diseases, such as epilepsy, depression, obesity, and for stroke rehabilitation. However, lack of precision and side-effects have hindered its efficacy and extension to new conditions. Achieving a better understanding of the relationship between VNS parameters and neural and physiological responses is therefore necessary to enable the design of personalized dosing procedures and improve precision and efficacy of VNS therapies.

Online Bayesian optimization of vagus nerve stimulation.

In bioelectronic medicine, neuromodulation therapies induce neural signals to the brain or organs, modifying their function. Stimulation devices capable of triggering exogenous neural signals using electrical waveforms require a complex and multi-dimensional parameter space to control such waveforms. Determining the best combination of parameters (waveform optimization or dosing) for treating a particular patient's illness is therefore challenging.

Image-Enhanced Endoscopy and Molecular Biomarkers Vs Seattle Protocol to Diagnose Dysplasia in Barrett's Esophagus.

Background & Aims: Dysplasia in Barrett's esophagus often is invisible on high-resolution white-light endoscopy (HRWLE). We compared the diagnostic accuracy for inconspicuous dysplasia of the combination of autofluorescence imaging (AFI)-guided probe-based confocal laser endomicroscopy (pCLE) and molecular biomarkers vs HRWLE with Seattle protocol biopsies.

Methods: Barrett's esophagus patients with no dysplastic lesions were block-randomized to standard endoscopy (HRWLE with the Seattle protocol) or AFI-guided pCLE with targeted biopsies for molecular biomarkers (p53 and cyclin A by immunohistochemistry; aneuploidy by image cytometry), with crossover to the other arm after 6 to 12 weeks.

Imputation of Ordinal Outcomes: A Comparison of Approaches in Traumatic Brain Injury.

Loss to follow-up and missing outcomes data are important issues for longitudinal observational studies and clinical trials in traumatic brain injury. One popular solution to missing 6-month outcomes has been to use the last observation carried forward (LOCF). The purpose of the current study was to compare the performance of model-based single-imputation methods with that of the LOCF approach.

Efficient Real-Time Monitoring of an Emerging Influenza Pandemic: How Feasible?

A prompt public health response to a new epidemic relies on the ability to monitor and predict its evolution in real time as data accumulate. The 2009 A/H1N1 outbreak in the UK revealed pandemic data as noisy, contaminated, potentially biased and originating from multiple sources. This seriously challenges the capacity for real-time monitoring.

Endoscopic measurement of gastric pH associates with persistent acid reflux in patients treated with proton-pump inhibitors for gastroesophageal reflux disease.

Background: Proton-pump inhibitors (PPIs) are the mainstay of gastroesophageal reflux disease (GERD) treatment, however, up to 30% of patients have a poor symptomatic response. PH-impedance is the gold standard to assess whether this is due to persistent acid reflux. We aimed to characterize clinical predictors of persistent esophageal acid reflux on PPIs including gastric pH measured during endoscopy.

GPseudoClust: deconvolution of shared pseudo-profiles at single-cell resolution.

Motivation: Many methods have been developed to cluster genes on the basis of their changes in mRNA expression over time, using bulk RNA-seq or microarray data. However, single-cell data may present a particular challenge for these algorithms, since the temporal ordering of cells is not directly observed. One way to address this is to first use pseudotime methods to order the cells, and then apply clustering techniques for time course data.

A meta-analysis of multiple matched aCGH/expression cancer datasets reveals regulatory relationships and pathway enrichment of potential oncogenes.

The copy numbers of genes in cancer samples are often highly disrupted and form a natural amplification/deletion experiment encompassing multiple genes. Matched array comparative genomics and transcriptomics datasets from such samples can be used to predict inter-chromosomal gene regulatory relationships. Previously we published the database METAMATCHED, comprising the results from such an analysis of a large number of publically available cancer datasets.

Transcriptomic profiling reveals three molecular phenotypes of adenocarcinoma at the gastroesophageal junction.

Cancers occurring at the gastroesophageal junction (GEJ) are classified as predominantly esophageal or gastric, which is often difficult to decipher. We hypothesized that the transcriptomic profile might reveal molecular subgroups which could help to define the tumor origin and behavior beyond anatomical location. The gene expression profiles of 107 treatment-naïve, intestinal type, gastroesophageal adenocarcinomas were assessed by the Illumina-HTv4.

Development and Validation of Confocal Endomicroscopy Diagnostic Criteria for Low-Grade Dysplasia in Barrett's Esophagus.

Objectives: Low-grade dysplasia (LGD) in Barrett's esophagus (BE) is generally inconspicuous on conventional and magnified endoscopy. Probe-based confocal laser endomicroscopy (pCLE) provides insight into gastro-intestinal mucosa at cellular resolution. We aimed to identify endomicroscopic features and develop pCLE diagnostic criteria for BE-related LGD.

Joining and splitting models with Markov melding.

Analysing multiple evidence sources is often feasible only via a modular approach, with separate submodels specified for smaller components of the available evidence. Here we introduce a generic framework that enables fully Bayesian analysis in this setting. We propose a generic method for forming a suitable joint model when submodels, and a convenient computational algorithm for fitting this joint model in stages, rather than as a single, monolithic model.

Branch-recombinant Gaussian processes for analysis of perturbations in biological time series.

Motivation: A common class of behaviour encountered in the biological sciences involves branching and recombination. During branching, a statistical process bifurcates resulting in two or more potentially correlated processes that may undergo further branching; the contrary is true during recombination, where two or more statistical processes converge. A key objective is to identify the time of this bifurcation (branch or recombination time) from time series measurements, e.

Metabolic regulation of pluripotency and germ cell fate through α-ketoglutarate.

An intricate link is becoming apparent between metabolism and cellular identities. Here, we explore the basis for such a link in an model for early mouse embryonic development: from naïve pluripotency to the specification of primordial germ cells (PGCs). Using single-cell RNA-seq with statistical modelling and modulation of energy metabolism, we demonstrate a functional role for oxidative mitochondrial metabolism in naïve pluripotency.

Modelling of psychosocial and lifestyle predictors of peripartum depressive symptoms associated with distinct risk trajectories: a prospective cohort study.

Perinatal depression involves interplay between individual chronic and acute disease burdens, biological and psychosocial environmental and behavioural factors. Here we explored the predictive potential of specific psycho-socio-demographic characteristics for antenatal and postpartum depression symptoms and contribution to severity scores on the Edinburgh Postnatal Depression Scale (EPDS) screening tool. We determined depression risk trajectories in 480 women that prospectively completed the EPDS during pregnancy (TP1) and postpartum (TP2).

GPseudoRank: a permutation sampler for single cell orderings.

Motivation: A number of pseudotime methods have provided point estimates of the ordering of cells for scRNA-seq data. A still limited number of methods also model the uncertainty of the pseudotime estimate. However, there is still a need for a method to sample from complicated and multi-modal distributions of orders, and to estimate changes in the amount of the uncertainty of the order during the course of a biological development, as this can support the selection of suitable cells for the clustering of genes or for network inference.

Clusternomics: Integrative context-dependent clustering for heterogeneous datasets.

Integrative clustering is used to identify groups of samples by jointly analysing multiple datasets describing the same set of biological samples, such as gene expression, copy number, methylation etc. Most existing algorithms for integrative clustering assume that there is a shared consistent set of clusters across all datasets, and most of the data samples follow this structure. However in practice, the structure across heterogeneous datasets can be more varied, with clusters being joined in some datasets and separated in others.

A comparison of machine learning and Bayesian modelling for molecular serotyping.

Background: Streptococcus pneumoniae is a human pathogen that is a major cause of infant mortality. Identifying the pneumococcal serotype is an important step in monitoring the impact of vaccines used to protect against disease. Genomic microarrays provide an effective method for molecular serotyping.

A graphical model approach visualizes regulatory relationships between genome-wide transcription factor binding profiles.

Integrated analysis of multiple genome-wide transcription factor (TF)-binding profiles will be vital to advance our understanding of the global impact of TF binding. However, existing methods for measuring similarity in large numbers of chromatin immunoprecipitation assays with sequencing (ChIP-seq), such as correlation, mutual information or enrichment analysis, are limited in their ability to display functionally relevant TF relationships. In this study, we propose the use of graphical models to determine conditional independence between TFs and showed that network visualization provides a promising alternative to distinguish 'direct' versus 'indirect' TF interactions.

BTR: training asynchronous Boolean models using single-cell expression data.

Background: Rapid technological innovation for the generation of single-cell genomics data presents new challenges and opportunities for bioinformatics analysis. One such area lies in the development of new ways to train gene regulatory networks. The use of single-cell expression profiling technique allows the profiling of the expression states of hundreds of cells, but these expression states are typically noisier due to the presence of technical artefacts such as drop-outs.

Pseudotime estimation: deconfounding single cell time series.

Motivation: Repeated cross-sectional time series single cell data confound several sources of variation, with contributions from measurement noise, stochastic cell-to-cell variation and cell progression at different rates. Time series from single cell assays are particularly susceptible to confounding as the measurements are not averaged over populations of cells. When several genes are assayed in parallel these effects can be estimated and corrected for under certain smoothness assumptions on cell progression.

The Discovery and Validation of Biomarkers for the Diagnosis of Esophageal Squamous Dysplasia and Squamous Cell Carcinoma.

The 5-year survival rate of esophageal cancer is less than 10% in developing countries, where more than 90% of these cancers are esophageal squamous cell carcinomas (ESCC). Endoscopic screening is undertaken in high incidence areas. Biomarker analysis could reduce the subjectivity associated with histologic assessment of dysplasia and thus improve diagnostic accuracy.

An optimal stratified Simon two-stage design.

In Phase II oncology trials, therapies are increasingly being evaluated for their effectiveness in specific populations of interest. Such targeted trials require designs that allow for stratification based on the participants' molecular characterisation. A targeted design proposed by Jones and Holmgren (JH) Jones CL, Holmgren E: 'An adaptive Simon two-stage design for phase 2 studies of targeted therapies', Contemporary Clinical Trials 28 (2007) 654-661.

Investigating inter-chromosomal regulatory relationships through a comprehensive meta-analysis of matched copy number and transcriptomics data sets.

Background: Gene regulatory relationships can be inferred using matched array comparative genomics and transcriptomics data sets from cancer samples. The way in which copy numbers of genes in cancer samples are often greatly disrupted works like a natural gene amplification/deletion experiment. There are now a large number of such data sets publicly available making a meta-analysis of the data possible.

Analysis of dysplasia in patients with Barrett's esophagus based on expression pattern of 90 genes.

Background & Aims: Diagnoses of dysplasia, based on histologic analyses, dictate management decisions for patients with Barrett's esophagus (BE). However, there is much intra- and inter-observer variation in identification of dysplasia-particularly low-grade dysplasia. We aimed to identify a biomarker that could be used to assign patients with low-grade dysplasia to a low- or high-risk group.