Identification of maternal continuous glucose monitoring metrics related to newborn birth weight in pregnant women with gestational diabetes.

Purpose: To identify the specific glucose metrics derived from maternal continuous glucose monitoring (CGM) data, which were associated with a higher percentile of offspring birth weight.

Methods: In this cohort study, we recruited singleton pregnant women with GDM who underwent CGM for 5-14 days at a mean of 28.8 gestational weeks between Jan 2017 and Nov 2018.

Identification of Circulating Diagnostic Biomarkers for Coronary Microvascular Disease in Postmenopausal Women Using Machine-Learning Techniques.

Coronary microvascular disease (CMD) is a common form of heart disease in postmenopausal women. It is not due to plaque formation but dysfunction of microvessels that feed the heart muscle. The majority of the patients do not receive a proper diagnosis, are discharged prematurely and must go back to the hospital with persistent symptoms.

The transcriptomic profile of endometrial receptivity in recurrent miscarriage.

Objective: To characterise the endometrial transcriptomic profiles of women who suffered recurrent miscarriage and to set the foundation for the development of an endometrial receptivity test that could predict the fate of subsequent pregnancies.

Study Design: This was a prospective multicentre cohort study performed at the Tommy's National Centre for Miscarriage Research in Birmingham, Saint Mary's Hospital in Manchester and Royal Devon & Exeter Hospital, United Kingdom. The study was conducted between December 2017 and December 2019.

Target Product Profile for an endometrial receptivity test: women's perspective.

Objective: To assess the women's views in relation to the characteristics of an endometrial receptivity test in the context of recurrent miscarriage with an overarching aim to guide the development of a Target Product Profile (TPP) based on minimum acceptable ("worst-case") and ideal ("best-case") features.

Study Design: This was a descriptive cross-sectional study involving a total of 131 women who answered questions related to the development of an endometrial receptivity test between December 2017 and May 2018. Women attending the recurrent miscarriage clinic at the Tommy's National Centre for Miscarriage Research in Birmingham, United Kingdom, were invited to participate.

pcaReduce: hierarchical clustering of single cell transcriptional profiles.

Background: Advances in single cell genomics provide a way of routinely generating transcriptomics data at the single cell level. A frequent requirement of single cell expression analysis is the identification of novel patterns of heterogeneity across single cells that might explain complex cellular states or tissue composition. To date, classical statistical analysis tools have being routinely applied, but there is considerable scope for the development of novel statistical approaches that are better adapted to the challenges of inferring cellular hierarchies.

A graph theoretical approach to data fusion.

The rapid development of high throughput experimental techniques has resulted in a growing diversity of genomic datasets being produced and requiring analysis. Therefore, it is increasingly being recognized that we can gain deeper understanding about underlying biology by combining the insights obtained from multiple, diverse datasets. Thus we propose a novel scalable computational approach to unsupervised data fusion.

Derivative processes for modelling metabolic fluxes.

Motivation: One of the challenging questions in modelling biological systems is to characterize the functional forms of the processes that control and orchestrate molecular and cellular phenotypes. Recently proposed methods for the analysis of metabolic pathways, for example, dynamic flux estimation, can only provide estimates of the underlying fluxes at discrete time points but fail to capture the complete temporal behaviour. To describe the dynamic variation of the fluxes, we additionally require the assumption of specific functional forms that can capture the temporal behaviour.

StochSens--Matlab package for sensitivity analysis of stochastic chemical systems.

Motivation: The growing interest in the role of stochasticity in biochemical systems drives the demand for tools to analyse stochastic dynamical models of chemical reactions. One powerful tool to elucidate performance of dynamical systems is sensitivity analysis. Traditionally, however, the concept of sensitivity has mainly been applied to deterministic systems, and the difficulty to generalize these concepts for stochastic systems results from necessity of extensive Monte Carlo simulations.