Biomedical data are becoming increasingly multimodal and thereby capture the underlying
complex relationships among biological processes. Deep learning (DL)-based data fusion …

High-throughput next-generation sequencing now makes it possible to generate a vast
amount of multi-omics data for various applications. These data have revolutionized …

Multimodal biomedical data fusion plays a pivotal role in distilling comprehensible and
actionable insights by seamlessly integrating disparate biomedical data from multiple …

Background A fused method using a combination of multi-omics data enables a
comprehensive study of complex biological processes and highlights the interrelationship of …

Although major advances in genomics have initiated an exciting new era of research, a lack
of information regarding cis-regulatory elements has limited the genetic improvement or …

Background For a sustainable food processing environment, robust and real-time monitoring
of pathogens is particularly important. Therefore, novel methods integrating metabolomics …

The abundance of artificial intelligence AI algorithms and growing computing power has
brought a disruptive revolution to the smart medical industry. Its powerful data abstraction …

The high cost of large-scale, high-coverage whole-genome sequencing has limited its
application in genomics and genetics research. The common approach has been to impute …

Motivation Multi-omics data provide a comprehensive view of gene regulation at multiple
levels, which is helpful in achieving accurate diagnosis of complex diseases like cancer …

With the exponential growth of multi-omics data, its integration and utilization have brought
unprecedented opportunities for the interpretation of gene regulation mechanisms and the …