Background: Determining drug–disease associations is an integral part in the process of drug development. However, the identification of drug–disease associations through wet experiments is costly and inefficient. Hence, the development of efficient and high-accuracy computational methods for predicting drug–disease associations is of great significance. Results: In this paper, we propose a novel computational method named as layer attention graph convolutional network (LAGCN) for the drug–disease association prediction. Specifically, LAGCN first integrates the known drug–disease associations, drug–drug similarities and disease–disease similarities into a heterogeneous network, and applies the graph convolution operation to the network to learn the embeddings of drugs and diseases. Second, LAGCN combines the embeddings from multiple graph convolution layers using an attention mechanism. Third, the unobserved drug–disease associations are scored based on the integrated embeddings. Evaluated by 5-fold cross-validations, LAGCN achieves an area under the precision–recall curve of 0.3168 and an area under the receiver–operating characteristic curve of 0.8750, which are better than the results of existing state-of-the-art prediction methods and baseline methods. The case study shows that LAGCN can discover novel associations that are not curated in our dataset. Conclusion: LAGCN is a useful tool for predicting drug–disease associations. This study reveals that embeddings from different convolution layers can reflect the proximities of different orders, and combining the embeddings by the attention mechanism can improve the prediction performances.

Abstract. Presumed source specificity of branched glycerol dialkyl glycerol tetraethers (brGDGTs) from bacteria thriving in soil/peat and isoprenoid GDGTs (iGDGTs) from aquatic organisms led to the development of several biomarker proxies for biogeochemical cycle and paleoenvironmental reconstructions. However, recent studies reveal that brGDGTs are also produced in aquatic environments besides soils and peat. Here we examined three cores from the Bohai Sea, and found distinct difference in brGDGT compositions varying with the distance from the Yellow River mouth. We thus propose an abundance ratio of hexamethylated to pentamethylated brGDGT (IIIa ∕ IIa) to evaluate brGDGT sources. The compilation of globally distributed 1354 marine sediments and 589 soils shows that the IIIa ∕ IIa ratio is generally < 0.59 in soils and 0.59–0.92 and > 0.92 in marine sediments with and without significant terrestrial inputs, respectively. Such disparity confirms the existence of two sources for brGDGTs, a terrestrial origin with lower IIIa ∕ IIa and a marine origin with higher IIIa ∕ IIa, which is likely attributed to a generally higher pH and the production of brGDGTs in cold deep water in marine waters. The application of the IIIa ∕ IIa ratio to the East Siberian Arctic Shelf proves it to be a sensitive source indicator for brGDGTs, which is helpful for accurate estimation of organic carbon source and paleoclimates in marine settings.

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