Precision cancer medicine promises to tailor clinical decisions to patients using genomic
information. Indeed, successes of drugs targeting genetic alterations in tumors, such as …

Identifying robust survival subgroups of hepatocellular carcinoma (HCC) will significantly
improve patient care. Currently, endeavor of integrating multi-omics data to explicitly predict …

Motivation Estimating the future course of patients with cancer lesions is invaluable to
physicians; however, current clinical methods fail to effectively use the vast amount of …

With the development of high-throughput biological techniques, high-dimensional omics
data have emerged. These molecular data provide a solid foundation for precision medicine …

Background This study aims to identify a predictive model to predict survival outcomes of
osteosarcoma (OS) patients. Methods A RNA sequencing dataset (the training set) and a …

Breast Cancer is a disease with high risk and mortality rate associated with female health.
The multi-omics data having genome, proteome, transcriptome, metabolome data, and …

BhGLM is a freely available R package that implements Bayesian hierarchical modeling for
high-dimensional clinical and genomic data. It consists of functions for setting up various …

Background Colon adenocarcinoma (COAD) is one of the common gastrointestinal
malignant diseases, with high mortality rate and poor prognosis due to delayed diagnosis …

PURPOSE: This study endeavors to build a deep learning (DL)-based model for predicting
disease progression in head and neck squamous cell carcinoma (HNSCC) patients by …

Motivation Complex diseases such as cancers often involve multiple types of genomic
and/or epigenomic abnormalities. Rapid accumulation of multiple types of omics data …