Results 41 to 50 of about 4,990,305 (257)

A drug combination prediction framework based on graph convolutional network and heterogeneous information

, 2022
Combination therapy, which can improve therapeutic efficacy and reduce side effects, plays an important role in the treatment of complex diseases. Yet, a large number of possible combinations among candidate compounds limits our ability to identify ...
Rao, Y, Lu, Y, Yang, Y, Chen, H
core   +1 more source

Self‐Assembled Monolayers in p–i–n Perovskite Solar Cells: Molecular Design, Interfacial Engineering, and Machine Learning–Accelerated Material Discovery

Advanced Materials, EarlyView.
This review highlights the role of self‐assembled monolayers (SAMs) in perovskite solar cells, covering molecular engineering, multifunctional interface regulation, machine learning (ML) accelerated discovery, advanced device architectures, and pathways toward scalable fabrication and commercialization for high‐efficiency and stable single‐junction and
Asmat Ullah, Ying Luo, Stefaan De Wolf
wiley   +1 more source

A Two-Stream Graph Convolutional Network Based on Brain Connectivity for Anesthetized States Analysis

IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2022
Investigating neural mechanisms of anesthesia process and developing efficient anesthetized state detection methods are especially on high demand for clinical consciousness monitoring.
Kun Chen, Tianli Xie, Li Ma, Andrew E. Hudson, Qingsong Ai, Quan Liu   +5 more
doaj   +1 more source

Organic Materials of Tomorrow: Horizons of Artificial Intelligence

Advanced Materials, EarlyView.
This review examines machine learning techniques accelerating the discovery of organic semiconductors by linking molecular structure to properties. Key methods include graph neural networks, generative models, and active learning. Applications to organic photovoltaics demonstrate practical impact.
Harold Mena, J. Terence Blaskovits, Kun‐Han Lin, Denis Andrienko   +3 more
wiley   +1 more source

Drug-target interaction prediction based on spatial consistency constraint and graph convolutional autoencoder

BMC Bioinformatics, 2023
Background Drug-target interaction (DTI) prediction plays an important role in drug discovery and repositioning. However, most of the computational methods used for identifying relevant DTIs do not consider the invariance of the nearest neighbour ...
Peng Chen, Haoran Zheng
doaj   +1 more source

Leak Detection in Water Supply Network Using a Data-Driven Improved Graph Convolutional Network

IEEE Access, 2023
Due to the complex correlation within data collection, it is a challenging task to detect leakage in the water supply network. The Graph Convolutional Network (GCN) has recently gained significant attention in correlation research. However, most existing
Suisheng Chen, Yun Wang, Wei Zhang, Hairong Zhang, Yuchen He   +4 more
doaj   +1 more source

Data‐Driven Materials Science for Energy‐Sustainable Applications

Advanced Materials, EarlyView.
Data‐driven approaches powered by artificial intelligence are transforming materials discovery for energy sustainability. This review examines how auto‐generated high‐quality materials databases and domain‐specific language models accelerate research in photovoltaics, thermoelectrics, batteries and magnetic materials. Applications involve extraction of
Jacqueline M. Cole
wiley   +1 more source

Beyond Low-pass Filtering: Graph Convolutional Networks with Automatic Filtering [PDF]

, 2022
Graph convolutional networks are becoming indispensable for deep learning from graph-structured data. Most of the existing graph convolutional networks share two big shortcomings.
Jiang, Jing, Jiang, J, Long, Guodong, Long, G, Zhang, C, Wu, Z, Pan, Shirui, Zhang, Chengqi, Wu, Zonghan, Pan, S   +9 more
core   +1 more source

On‐Chip Photonic Neural Network Architectures

Advanced Optical Materials, EarlyView.
This review presents a comprehensive overview of on‐chip photonic neural network architectures, covering key photonic building blocks, representative network types, and emerging applications. Recent advances, implementation challenges, and future directions are examined, highlighting the potential of integrated photonics to enable ultrafast, energy ...
Seokjin Hong, Berkay Neseli, Seungyeon Cho, Seungsoo Lee, Yeonjun Kim, Seungyoon Choi, Hyo‐Hoon Park, Hamza Kurt   +7 more
wiley   +1 more source

A Modulation Classification Algorithm Based on Feature-Embedding Graph Convolutional Network

IEEE Access
Deep-learning is widely used in modulation classification to reduce labor and improve the efficiency. Graph convolutional network (GCN) is a type of feature extraction network for graph data.
Huali Zhu, Hua Xu, Yunhao Shi, Yue Zhang, Lei Jiang   +4 more
doaj   +1 more source