Suchergebnisse - "variational graph autoencoder (VGAE)"

Accurate identification of snoRNA targets using variational graph autoencoder to advance the redevelopment of traditional medicines

Autoren: Zhina Wang Yangyuan Chen Hongming Ma et al.

Quelle: Front Pharmacol
Frontiers in Pharmacology, Vol 15 (2025)

Schlagwörter: Pharmacology, lung cancer, redevelopment of traditional medicines, variational graph autoencoder (VGAE), snoRNA therapeutic targets, Therapeutics. Pharmacology, RM1-950, artificial intelligence (AI)

Zugangs-URL: https://doaj.org/article/c553efc486264dfca34df3bbee44c6bc

Graph-Deep-Learning-Based Inference of Fine-Grained Air Quality From Mobile IoT Sensors

Autoren: Tien Huu Do Evaggelia Tsiligianni Xuening Qin et al.

Weitere Verfasser: Tien Huu Do Evaggelia Tsiligianni Xuening Qin et al.

Quelle: IEEE Internet of Things Journal. 7:8943-8955

Schlagwörter: IoT, variational graph autoencoder (VGAE), Smart Cities, 13. Climate action, nternet of Things (IoT), 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Deep learning, 02 engineering and technology, 7. Clean energy

Zugangs-URL: https://dblp.uni-trier.de/db/journals/iotj/iotj7.html#DoTQHMPD20
https://ieeexplore.ieee.org/abstract/document/9106368
https://biblio.ugent.be/publication/8676438
https://researchportal.vub.be/nl/publications/graph-deep-learning-based-inference-of-fine-grained-air-quality-f
https://biblio.vub.ac.be/vubir/graphdeeplearningbased-inference-of-finegrained-air-quality-from-mobile-iot-sensors(c1df178b-dd01-4bce-ab81-ec9243b2b964).html