Self-supervised graph representation learning integrates multiple molecular networks and decodes gene-disease relationships.

Leveraging molecular networks to discover disease-relevant modules is a long-standing challenge. With the accumulation of interactomes, there is a pressing need for powerful computational approaches to handle the inevitable noise and context-specific nature of biological networks. Here, we introduce Graphene, a two-step self-supervised representation learning framework tailored to concisely integrate multiple molecular networks and adapted to gene functional analysis via downstream re-training.

Segmental duplication as potential biomarkers for non-invasive prenatal testing of aneuploidies.

Background: Segmental duplication (SD) regions are distinct targets for aneuploidy detection owing to the virtual elimination of amplification bias. The difficulty of searching SD sequences for assay design has hampered their applications.

Methods: We developed a computational program, ChAPDes, which integrates SD searching, refinement, and design of specific PCR primer/probe sets in a pipeline to remove most of the manual work.

Broad-Spectrum Profiling of Drug Safety via Learning Complex Network.

Drug safety is a severe clinical pharmacology and toxicology problem that has caused immense medical and social burdens every year. Regretfully, a reproducible method to assess drug safety systematically and quantitatively is still missing. In this study, we developed an advanced machine learning model for de novo drug safety assessment by solving the multilayer drug-gene-adverse drug reaction (ADR) interaction network.

Karst rocky desertification progress: Soil calcium as a possible driving force.

Karst rocky desertification is a severe irreversible ecosystem failure. The karst ecosystem is so fragile that it is vulnerable to environmental changes, degrading into rocky desertification. Prior studies revealed the potential connections between the soil bacterial community, the edaphic properties and the aboveground vegetation cover in the karst ecosystem.

ADReCS-Target: target profiles for aiding drug safety research and application.

Delivering safe and effective therapeutic treatment to patients is one of the grand challenges in modern medicine. However, drug safety research has been progressing slowly in recent years, compared to other fields such as biotechnologies and precision medicine, due to the mechanistic complexity of adverse drug reactions (ADRs). To fill up this gap, we develop a new database, the Adverse Drug Reaction Classification System-Target Profile (ADReCS-Target, http://bioinf.