Cancer is thought to be caused by the accumulation of driver genetic mutations. Therefore, identifying cancer driver genes plays a crucial role in understanding the molecular mechanism of cancer and developing precision therapies and biomarkers. In this work, we propose a Multi-Task learning method, called MTGCN, based on the Graph Convolutional Network to identify cancer driver genes. First, we augment gene features by introducing their features on the protein-protein interaction (PPI) network. After that, the multi-task learning framework propagates and aggregates nodes and graph features from input to next layer to learn node embedding features, simultaneously optimizing the node prediction task and the link prediction task. Finally, we use a Bayesian task weight learner to balance the two tasks automatically. The outputs of MTGCN assign each gene a probability of being a cancer driver gene. Our method and the other four existing methods are applied to predict cancer drivers for pan-cancer and some single cancer types. The experimental results show that our model shows outstanding performance compared with the state-of-the-art methods in terms of the area under the Receiver Operating Characteristic (ROC) curves and the area under the precision-recall curves.
The MTGCN is freely available via https://github.com/weiba/MTGCN.
Honeybee (Apis mellifera) ingestion of toxic nectar plants can threaten their health and survival. However, little is known about how to help honeybees mitigate the effects of toxic nectar plant poisoning. We exposed honeybees to different concentrations of Bidens pilosa flower extracts and found that B. pilosa exposure significantly reduced honeybee survival in a dose‐dependent manner. By measuring changes in detoxification and antioxidant enzymes and the gut microbiome, we found that superoxide dismutase, glutathione‐S‐transferase and carboxylesterase activities were significantly activated with increasing concentrations of B. pilosa and that different concentrations of B. pilosa exposure changed the structure of the honeybee gut microbiome, causing a significant reduction in the abundance of Bartonella (p < 0.001) and an increase in Lactobacillus. Importantly, by using Germ‐Free bees, we found that colonization by the gut microbes Bartonella apis and Apilactobacillus kunkeei (original classification as Lactobacillus kunkeei) significantly increased the resistance of honeybees to B. pilosa and significantly upregulated bee‐associated immune genes. These results suggest that honeybee detoxification systems possess a level of resistance to the toxic nectar plant B. pilosa and that the gut microbes B. apis and A. kunkeei may augment resistance to B. pilosa stress by improving host immunity.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.