Background The COVID-19 pandemic is a significant public health crisis that is hitting hard
on people's health, well-being, and freedom of movement, and affecting the global economy …

Many real-world networks belong to the kind that evolves over time. So it is very meaningful
and challenging to predict whether the link will occur in the network of future time. In this …

In real-world machine learning applications, unlabeled training data are readily available,
but labeled data are expensive and hard to obtain. Therefore, semi-supervised learning …

A tissue sample is a valuable resource for understanding a patient's symptoms and health
status in relation to tumor growth. Recent research seeks to establish a connection between …

Amidst prevailing healthcare challenges, a dynamic solution emerges, fusing knowledge
graph technology, clinical trials optimization, dataspace integration, and AI innovation. This …

Foundation Models are pivotal in advancing artificial intelligence, driving notable progress
across diverse areas. When merged with dataspace, these models enhance our capability to …

Predicting the onset time of adverse drug events can substantially lessen the negative
impact on the prognosis of cancer patients who are often subject of aggressive and highly …

Objective: Lung cancer exhibits unpredictable recurrence in low-stage tumors and variable
responses to different therapeutic interventions. Predicting relapse in early-stage lung …

Large networks are quintessential to bioinformatics, knowledge graphs, social network
analysis, and graph-based learning. CompositeView is a Python-based open-source …

The scarcity of high-quality annotations in many application scenarios has recently led to an
increasing interest in devising learning techniques that combine unlabeled data with labeled …