Following the widespread use of deep learning for genomics, deep generative modeling is
also becoming a viable methodology for the broad field. Deep generative models (DGMs) …

In recent years, the concept of federated machine learning has been actively driven by
scientists to ease the privacy concerns of data owners. Currently, the combination of …

Genome-wide association studies (GWAS) serve as a crucial tool for identifying genetic
factors associated with specific traits. However, ethical constraints prevent the direct …

High-throughput sequencing allowed the discovery of many disease variants, but nowadays
it is becoming clear that the abundance of genomics data mostly just moved the bottleneck …

To fully exploit enormous data generated by intelligent devices in edge computing, edge
federated learning (EFL) is envisioned as a promising solution. The distributed collaborative …

Genomic data has enabled the rapid growth of the US bioeconomy and is valuable to the
individual, industry, and government because it has multiple intrinsic properties that in …

Kinship relationship estimation plays a significant role in today's genome studies. Since
genetic data are mostly stored and protected in different silos, retrieving the desirable …

Abstract Artificial Intelligence (AI) and Machine Learning (ML) methods have been applied
significantly in modern healthcare systems in the last few years. AI and its subfields, such as …

Learning on graphs is becoming prevalent in a wide range of applications including social
networks, robotics, communication, medicine, etc. These datasets belonging to entities often …

It is challenging to share and aggregate biomedical data distributed among multiple
institutions or computing resources due to various concerns including data privacy, security …