Biomarkers, also known as biological markers, are substances like transcripts,
deoxyribonucleic acid (DNA), genes, proteins, and metabolites that indicate whether a …

Prognostic biomarkers are of great significance to predict the outcome of patients with
cancer, to guide the clinical treatments, to elucidate tumorigenesis mechanisms, and offer …

Background Cutaneous melanoma is one of the most aggressive and lethal skin cancers. It
is greatly important to identify prognostic biomarkers to guide the clinical management …

In the current state of genomics and biomedical research, the utilization of Artificial
Intelligence (AI), Machine Learning (ML) and Deep Learning (DL) have emerged as …

Background: Survival analysis is a primary problem before clinical treatments to cancer
patients after their operations. In order to make this kind of analysis simple, many …

Biomedical multi-modality data (also named multi-omics data) refer to data that span
different types and derive from multiple sources in clinical practices (eg gene sequences …

Patients with cancer with different molecular characterization and subtypes result in different
response to anticancer therapeutics and survival. To identify features that are associated …

Comprehensive genomic analyses of cancers have revealed substantial intrapatient
molecular heterogeneities that may explain some instances of drug resistance and treatment …

Clustering is the process of grouping different data objects based on similar properties.
Clustering has applications in various case studies from several fields such as graph theory …

Aim: To establish a web server that can mutually validate prognostic biomarkers of cervical
cancer. Methods: Four datasets including expression profiling and relative clinical follow-up …