Drug discovery and development is a complex and costly process. Machine learning
approaches are being investigated to help improve the effectiveness and speed of multiple …

Single-cell RNA sequencing (scRNA-seq) has become a routinely used technique to
quantify the gene expression profile of thousands of single cells simultaneously. Analysis of …

Gastric cancer (GC) represents a significant burden of cancer-related mortality worldwide,
underscoring an urgent need for the development of early detection strategies and precise …

The incorporation of machine learning methods into proteomics workflows improves the
identification of disease-relevant biomarkers and biological pathways. However, machine …

Biomedical data are amassed at an ever-increasing rate, and machine learning tools that
use prior knowledge in combination with biomedical big data are gaining much traction 1, 2 …

Abstract Motivation Knowledge graphs (KGs) are being adopted in industry, commerce and
academia. Biomedical KG presents a challenge due to the complexity, size and …

The incorporation of data analytics in the healthcare industry has made significant progress,
driven by the demand for efficient and effective big data analytics solutions. Knowledge …

Biomedical data is accumulating at a fast pace and integrating it into a unified framework is a
major challenge, so that multiple views of a given biological event can be considered …

To comprehensively understand tissue and organism physiology and pathophysiology, it is
essential to create complete three-dimensional (3D) cellular maps. These maps require …

Electronic health records (EHR) are collected as a routine part of healthcare delivery, and
have great potential to be utilized to improve patient health outcomes. They contain multiple …