With the development of modern high-throughput omic measurement platforms, it has
become essential for biomedical studies to undertake an integrative (combined) approach to …

Selecting among competing statistical models is a core challenge in science. However, the
many possible approaches and techniques for model selection, and the conflicting …

Background Risk stratification and therapeutic decision-making for myelodysplastic
syndromes (MDS) are based on the International Prognostic Scoring System–Revised (IPSS …

Sparse model identification enables the discovery of nonlinear dynamical systems purely
from data; however, this approach is sensitive to noise, especially in the low-data limit. In this …

By combining metal nodes with organic linkers we can potentially synthesize millions of
possible metal–organic frameworks (MOFs). The fact that we have so many materials opens …

Objective Type 1 diabetes (T1D) is characterised by islet autoimmunity and beta cell
destruction. A gut microbiota–immunological interplay is involved in the pathophysiology of …

Feature selection technique is a knowledge discovery tool which provides an understanding
of the problem through the analysis of the most relevant features. Feature selection aims at …

We introduce a simple new approach to variable selection in linear regression, with a
particular focus on quantifying uncertainty in which variables should be selected. The …

Variable importance (VI) tools describe how much covariates contribute to a prediction
model's accuracy. However, important variables for one well-performing model (for example …

While gut microbiome and host gene regulation independently contribute to gastrointestinal
disorders, it is unclear how the two may interact to influence host pathophysiology. Here we …