Results 111 to 120 of about 24,295 (253)

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

HSPAN-GNN-based fault detection for power transmission lines

EURASIP Journal on Advances in Signal Processing
To supply uninterrupted electric power is extremely crucial for the economy and daily life. The traditional manual inspection method for power transmission line fault detection has many disadvantages.
Ximing Zhang, Zhiming Wang, Huan Xu, Liang Zhao, Yupeng Wei, Jiguang Zhao, Bing Tian, Na Zhang   +7 more
doaj   +1 more source

Self-supervised Hybrid Graph Neural Network for Session-Based Recommendation [PDF]

Jisuanji kexue yu tansuo
Session-based recommendation aims to predict user actions based on anonymous sessions. Most of the existing session recommendation algorithms based on graph neural network (GNN) only extract user preferences for the current session, but ignore the high ...
ZHANG Yusong, XIA Hongbin, LIU Yuan
doaj   +1 more source

Large‐Scale Machine Learning to Screen for Small‐Molecule Senolytics

Advanced Intelligent Discovery, EarlyView.
A consistent workflow underpins all experiments in this study. A dedicated model‐selection dataset first identifies optimal hyperparameters for each algorithm. Models are then trained and rigorously evaluated on independent sets of molecules using the senolytic ratio SR. Comprehensive hyperparameter exploration across SMILES representations, task types,
Alexis Dougha, Jesus Gil, Pedro J. Ballester   +2 more
wiley   +1 more source

Accelerating Discovery of Organic Molecular Crystals via Materials Informatics and Autonomous Experiments

Advanced Intelligent Discovery, EarlyView.
Materials informatics and autonomous experimentation are transforming the discovery of organic molecular crystals. This review presents an integrated molecule–crystal–function–optimization workflow combining machine learning, crystal structure prediction, and Bayesian optimization with robotic platforms.
Takuya Taniguchi, Kazuki Ishizaki, Ryo Fukasawa   +2 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

Advanced Intelligent Systems, EarlyView.
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella, Angelo Cardellicchio, Cosimo Patruno, Massimiliano Nitti, Nicola Pedrocchi, Vito Renò   +5 more
wiley   +1 more source

Enhanced exploration in reinforcement learning using graph neural network based intrinsic reward mechanism

Scientific Reports
We propose a Graph Neural Network-based Intrinsic Reward Learning (GNN-IRL) framework to address the exploration–exploitation trade-off in Reinforcement Learning (RL). This approach leverages the structural modeling capabilities of Graph Neural Networks (
J. Arun Pandian, Ramkumar Thirunavukarasu, Rajganesh Nagarajan   +2 more
doaj   +1 more source

Comparing the Latent Features of Universal Machine‐Learning Interatomic Potentials

Advanced Intelligent Systems, EarlyView.
This study quantitatively assesses how universal machine‐learning interatomic potentials encode the chemical space into latent features, showing unique model‐specific representations with high cross‐model reconstruction errors. It explores how training datasets, protocols, and targets affect these encodings.
Sofiia Chorna, Davide Tisi, Cesare Malosso, Wei Bin How, Michele Ceriotti, Sanggyu Chong   +5 more
wiley   +1 more source

AGS-GNN: Attribute-guided Sampling for Graph Neural Networks

Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
The paper has been accepted to KDD'24 in the research ...
Siddhartha Shankar Das, S. M. Ferdous, Mahantesh M. Halappanavar, Edoardo Serra, Alex Pothen   +4 more
openaire   +2 more sources