Recent studies have extensively used deep learning algorithms to analyze gene expression
to predict disease diagnosis, treatment effectiveness, and survival outcomes. Survival …

Background Multiple genes were previously identified to be associated with cervical cancer;
however, the genetic architecture of cervical cancer remains unknown and many potential …

Understanding the intricate relationships between diseases is critical for both prevention
and recovery. However, there is a lack of suitable methodologies for exploring the …

Background Previous cancer prognostic prediction models often consider only the most
important transcriptomic expressions, and their power is limited. It is unknown whether …

Traditionally, datasets with multiple censored time-to-events have not been utilized in
multivariate analysis because of their high level of complexity. In this paper, we propose the …

Motivation Network-based analysis of biomedical data has been extensively studied over
the last decades. As a successful application, gene networks have been used to illustrate …