Breast cancer is prone to form bone metastases and subsequent skeletal‐related events
(SREs) dramatically decrease patients' quality of life and survival. Prediction and early …

We propose AFTNet, a novel network‐constraint survival analysis method based on the
Weibull accelerated failure time (AFT) model solved by a penalized likelihood approach for …

Breast cancer is one of the most common cancers in women worldwide, which causes an
enormous number of deaths annually. However, early diagnosis of breast cancer can …

Background Recent studies have indicated that a special class of long non-coding RNAs
(lncRNAs), namely Transcribed-Ultraconservative Regions are transcribed from specific …

Identifying relevant genomic features that can act as prognostic markers for building
predictive survival models is one of the central themes in medical research, affecting the …

Objective. The aim of this study is to design a weighted co‐expression network and build
gene expression signature‐based nomogram (GESBN) models for predicting the likelihood …

Complex, distributed, and dynamic sets of clinical biomedical data are collectively referred to
as multimodal clinical data. In order to accommodate the volume and heterogeneity of such …

188 Symbolic Approaches to Modeling and Analysis of Biological Systems properties. In
biomedicine, the study of multi-omic pathway interactions, together with clinical and …

Data streams for a personalised breast cancer programme could include collections of
image data, tumour genome sequencing, likely at the single cell level, and liquid biopsies …

Background: Advances in sequencing technologies have allowed collection of massive
genome-wide information that substantially advances lung cancer diagnosis and prognosis …