Life sciences researchers using artificial intelligence (AI) are under pressure to innovate
faster than ever. Large, multilevel, and integrated data sets offer the promise of unlocking …

Plants are constantly exposure to pathogens such as virus, bacteria and fungi. Plant
diseases caused by pathogens lead significant crop yield loss globally. Numerous …

Objective To discover diverse genotype-phenotype associations affiliated with Type 2
Diabetes Mellitus (T2DM) via genome-wide association study (GWAS) and phenome-wide …

This study attempts to develop a machine learning model to predict the concrete slump as a
function of mix proportions, taking advantage of the 3599 observations of industrially …

Timely stroke diagnosis and intervention are necessary considering its high prevalence.
Previous studies have mainly focused on stroke prediction with balanced data. Thus, this …

Scientific collaborations benefit from sharing information and data from distributed sources,
but protecting privacy is a major concern. Researchers, funders, and the public in general …

Diabetic retinopathy (DR) is an important disease leading to blindness in humans, attracting
a lot of research interests. Previous breakthrough research findings rely on deep learning …

Background: Functional outcomes after acute ischemic stroke are of great concern to
patients and their families, as well as physicians and surgeons who make the clinical …

Based on sensory conflict theory, motion sickness is strongly related to the information
processing capacity or resources of the brain to cope with the multi-sensory stimuli …

Objectives: We propose a communication-efficient transfer learning approach (COMMUTE)
that effectively incorporates multi-site healthcare data for training a risk prediction model in a …