The deployment of machine learning for tasks relevant to complementing standard of care
and advancing tools for precision health has gained much attention in the clinical …

Causality is fundamental to science, but it appears in several different forms. One is
relativistic causality, which is tied to a spacetime structure and forbids signaling outside the …

Causal modeling is a tool for generating causal explanations of observed correlations and
has led to a deeper understanding of correlations in quantum networks. Existing frameworks …

Abstract Machine learning is increasingly used to discover diagnostic and prognostic
biomarkers from high-dimensional molecular data. However, a variety of factors related to …

Machine learning is increasingly used to discover diagnostic and prognostic biomarkers
from high-dimensional molecular data. However, a variety of factors related to experimental …

Many ecologists increasingly advocate for research frameworks centered on the use of 'big
data'to address anthropogenic impacts on ecosystems. Yet, experiments are often …

Background: Radiomics refers to the acquisition of traces of quantitative features that are
usually non-perceptible to human vision and are obtained from different imaging techniques …

Improving nutritional status during pregnancy is a global interest. Frequently, women either
fail to meet or exceed nutrient recommendations. Current strategies to improve maternal …

Many modern research fields increasingly rely on collecting and analysing massive, often
unstructured, and unwieldy datasets. Consequently, there is growing interest in machine …

Chronic low back pain (cLBP) afflicts 8. 2% of adults in the United States, and is the leading
global cause of disability. Neuropsychiatric co-morbidities including anxiety, depression …