Results 81 to 90 of about 22,751 (263)

AI‐Driven Cancer Multi‐Omics: A Review From the Data Pipeline Perspective

Advanced Intelligent Discovery, EarlyView.
The exponential growth of cancer multi‐omics data brings opportunities and challenges for precision oncology. This review systematically examines AI's role in addressing these challenges, covering generative models, integration architectures, Explainable AI for clinical trust, clinical applications, and key directions for clinical translation.
Shilong Liu, Shunxiang Li, Kun Qian
wiley   +1 more source

Synthesis of Doped g‐C3N4 Photonic Crystals for Enhanced Light‐Driven Hydrogen Production from Catalytic Water‐Splitting

Advanced Energy & Sustainability Research
Dopants are frequently used to improve graphitic carbon nitride (gCN) photoactivity. As a doping source, phosphomolybdic acid (PMA) can activate doping sites inside the gCN lattice, resulting in 2D Mo:P‐gCN porous material.
Simon Y. Djoko T., Sunil Kwon, Prasenjit Das, Vincent Weigelt, Warisha Tahir, Babu Radhakrishnan, Klaus Schwarzburg, Arne Thomas, Michael Schwarze, Reinhard Schomäcker   +9 more
doaj   +1 more source

Deep Learning–Based Extraction of Promising Material Groups and Common Features from High‐Dimensional Data: A Case of Optical Spectra of Inorganic Crystals

Advanced Intelligent Discovery, EarlyView.
We report a novel interpretation method for deep learning models based on feature extraction and clustering. Applying this method to an atomistic line graph neural network (ALIGNN) model trained on optical absorption spectra of 2,681 inorganic compounds obtained from first‐principles calculations, we successfully identify key factors underlying ...
Akira Takahashi, Yu Kumagai, Arata Takamatsu, Fumiyasu Oba   +3 more
wiley   +1 more source

Prognostic value of MET gene copy number and protein expression in patients with surgically resected non-small cell lung cancer: a meta-analysis of published literatures. [PDF]

PLoS ONE, 2014
BACKGROUND: The prognostic value of the copy number (GCN) and protein expression of the mesenchymal-epithelial transition (MET) gene for survival of patients with non-small cell lung cancer (NSCLC) remains controversial.
Baoping Guo, Hong Cen, Xiaohong Tan, Wenjian Liu, Qing Ke   +4 more
doaj   +1 more source

Crystal Structure Prediction of Inorganic Materials: A Benchmark and Modern Evaluation

Advanced Intelligent Discovery, EarlyView.
Predicting a crystal’s structure from composition alone is a long‐standing challenge in materials discovery. The CSP180 benchmark of 180 inorganic crystals evaluates thirteen crystal structure prediction algorithms requiring no density functional theory (DFT) against DFT‐based baselines across twelve metrics.
Lai Wei, Sadman Sadeed Omee, Rongzhi Dong, Nihang Fu, Yuqi Song, Edirisuriya M. Dilanga Siriwardane, Meiling Xu, Ming Hu, Chris Wolverton, Jianjun Hu   +9 more
wiley   +1 more source

Exploiting Edge Semantics in Job Shop Scheduling Problem With Heterogeneous Graph Transformers

Advanced Intelligent Systems, EarlyView.
A heterogeneous graph transformer (HGT) is introduced for reinforcement learning‐based job shop scheduling by explicitly distinguishing precedence and machine‐contention relations through edge‐type‐specific attention. The proposed framework learns richer scheduling representations, improves decision quality over homogeneous graph models, and highlights
Bulent Soykan, Fatih Kasimoglu
wiley   +1 more source

Prolonging fruit shelf-life with a graphitic carbon nitride (GCN) incorporating biopolymer composite: layer-by-layer assembled coating [PDF]

Sustainable Food Technology
This study presents a novel, biodegradable, and multifunctional layer-by-layer (LbL) coating composed of sodium alginate (SA), chitosan (CAS), and graphitic carbon nitride (GCN) for efficient fruit preservation.
Md. Sajib Hossain, Md. Didarul Islam, Anik Das, M. Mehedi Hasan, Md. Mahfuzur Rahman, Samrat Mukhopadhyay   +5 more
doaj   +1 more source

SVD-GCN: A Simplified Graph Convolution Paradigm for Recommendation

, 2022
With the tremendous success of Graph Convolutional Networks (GCNs), they have been widely applied to recommender systems and have shown promising performance.
Peng, Shaowen, Sugiyama, Kazunari, Mine, Tsunenori   +2 more
core  

Graph Neural Network‐Based Reinforcement Learning for Decentralized Multi‐Robot Manipulation

Advanced Intelligent Systems, EarlyView.
Robot arms lifting a large object face a trade‐off: centralized controllers explode in parameters, while decentralized ones cannot coordinate. A GNN resolves this—each arm runs its own network but acts on the full team state, achieving centralized‐level coordination with decentralized execution. Trained across team sizes, a single policy scales to four‐
Tong Chen, Bo Fu, Dawn Tilbury, Kira Barton   +3 more
wiley   +1 more source

Rapoudok/GCN-herbal-medicine: 0.0.1

<p>Predicting new herbal prescriptions using GCNs</p ...
Tae-Hyoung Kim
core   +1 more source