We present a novel joint prognostic framework for the integrated analysis and uncertainty quantification of longitudinal (i.e., multi-sensor degradation signals) data and time-to-event data.

This important study presents a new method for longitudinally tracking cells in two-photon imaging data that addresses the specific challenges of imaging neurons in the developing cortex. It provides ...

Compact representation of graph data is a fundamental problem in pattern recognition and machine learning area. Recently, graph neural networks (GNNs) have been widely studied for graph-structured ...

After the script completes there should be a data folder in the biokg folder This data folder will have 4 folders sources which contains the sources used to compile BioKG preprocessed which contains ...

Discussion Longitudinal data from this observational study did not show an increase in CV risk factors in patients who develop PsA compared with controls. In fact, the PsA group experienced a smaller ...

☂️ Scikit-longitudinal (Sklong) is an open-source Python library & Scikit-Learn API compliant, tailored to longitudinal machine learning classification tasks. It is ideal for researchers, data ...

Here, molecular graphs derived from the one-electron density matrix are introduced within a more general effort to explore whether incorporating electronic structure awareness allows a single model to ...

Velsera's Global Data Network Redefines How Pharma and Biotech Access Real-World, Longitudinal Clinicogenomic Data at Scale By Velsera Jun 24, 2025 Velsera By Velsera ...

The cost of attending college has more young people (and their financially stressed parents) questioning whether it’s even worth it to pursue a degree. The answer, it turns out, could largely… ...

ALSFRS-R subscores were defined for bulbar, motor and respiratory domains. Longitudinal models were used to visualise fitted total ALSFRS-R and ALSFRS-R subscore progression. In addition, the ...