A deep learning framework for predicting disease-gene associations with functional modules and graph augmentation.

Background: The exploration of gene-disease associations is crucial for understanding the mechanisms underlying disease onset and progression, with significant implications for prevention and treatment strategies. Advances in high-throughput biotechnology have generated a wealth of data linking diseases to specific genes. While graph representation learning has recently introduced groundbreaking approaches for predicting novel associations, existing studies always overlooked the cumulative impact of functional modules such as protein complexes and the incompletion of some important data such as protein interactions, which limits the detection performance.

Ca substitution improves the catalytic activity of perovskite LaCoO toward toluene: comprehension of electronic structure alteration.

For perovskite LaCaCoO (Ca-, = 0-0.3), Ca-0.2 with the closest O p band center to the Fermi level, displays the best catalytic activity for toluene oxidation.

Development of novel multifunctional adsorbent by effectively hosting both zwitterionic surfactant and hydrated ferric oxides in montmorillonite.

Intercalating various functional species into the interlayer space is an effective strategy to multi-functionalize 2D materials (e.g., montmorillonite, Mnt), but general limitations have emerged therefrom: (1) various intercalated species compete for the limited interlayer space, and (2) the neighboring intercalated species probably inhibit each other's reactivity.