Learning Developmental Age From 3D Infant Kinetics Using Adaptive Graph Neural Networks by Daniel Holmberg, Manu Airaksinen, Viviana Marchi, Andrea Guzzetta, Anna Tuiskula, Leena Haataja, Sampsa Vanhatalo, Teemu Roos

“…These data are modeled using adaptive graph convolutional networks (AAGCNs), able to capture the spatio-temporal dependencies in infant movements. …”
Get full text

Multiplex Networks and Pan-Cancer Multiomics-Based Driver Gene Identification Using Graph Neural Networks by Xingyi Li, Junming Li, Jun Hao, Xingyu Liao, Min Li, Xuequn Shang

“…Identifying cancer driver genes has paramount significance in elucidating the intricate mechanisms underlying cancer development, progression, and therapeutic interventions. Abundant omics data and interactome networks provided by numerous extensive databases enable the application of graph deep learning techniques that incorporate network structures into the deep learning framework. …”
Get full text

Bind: large-scale biological interaction network discovery through knowledge graph-driven machine learning by Naafey Aamer, Muhammad Nabeel Asim, Aamer Iqbal Bhatti, Andreas Dengel

“…Methods We developed BIND (Biological Interaction Network Discovery), a comprehensive framework utilizing 11 Knowledge Graph Embedding Methods evaluated on 8 million interactions across 30 biological relationships and 129,000 nodes. …”
Get full text

Identifying key psychological characteristics among Chinese individuals with eating disorders: an exploratory graph and network analysis by Liyun Zheng, Chao Chen, Darong Zhang, Xueni Li, Shuxia Geng, Qingmei Kong, Tianmei Si

“…Abstract Background Interventions targeting core characteristics of eating disorders (EDs) can effectively alleviate symptoms. …”
Get full text

Human autonomy teaming-based safety-aware navigation through bio-inspired and graph-based algorithms by Timothy Sellers, Tingjun Lei, Chaomin Luo, Zhuming Bi, Gene Eu Jan

“…Central to our approach is a hybrid graph system that integrates the Generalized Voronoi Diagram (GVD) with spatio-temporal graphs, effectively combining human feedback, environmental factors, and key waypoints. …”
Get full text

Hybrid Graph Representation Learning for Carotid Artery Stenosis Detection Based on Multimodal Retinal OCTA Images by Wenting Lan, Jinkui Hao, Shengjun Zhou, Jingfeng Zhang, Shaodong Ma, Yitian Zhao

“…Rapid and precise detection of CAS is crucial for early intervention and reducing ischemic stroke incidence. …”
Get full text

AutoMEX: Streamlining material extrusion with AI agents powered by large language models and knowledge graphs by Haolin Fan, Junlin Huang, Jilong Xu, Yifei Zhou, Jerry Ying Hsi Fuh, Wen Feng Lu, Bingbing Li

“…AutoMEX utilizes a knowledge graph (KG) derived from the scientific literature to enable LLMs to provide expert recommendations on material selection, process parameters, and design considerations, thereby improving accessibility and efficiency. …”
Get full text

Nonsuicidal self-injury prediction with pain-processing neural circuits using interpretable graph neural network by Sichu Wu, Yuan Xue, Yaming Hang, Ya Xie, Pei Zhang, Minlu Liang, Yuan Zhong, Chun Wang

“…Our findings suggest altered pain processing as a key mechanism in NSSI, providing insights for potential neural modulation intervention strategies.…”
Get full text

Effects of exercise intervention on tobacco dependence: a meta-analysis by Jinzhi Xu, Shiyue Zhang, Zichao Chen, Zhusheng Wu

Get full text

AI modeling for outbreak prediction: A graph-neural-network approach for identifying vancomycin-resistant enterococcus carriers. by Gregor Donabauer, Anca Rath, Aila Caplunik-Pratsch, Anja Eichner, Jürgen Fritsch, Martin Kieninger, Susanne Gaube, Wulf Schneider-Brachert, Udo Kruschwitz, Bärbel Kieninger

“…We used data from 8,372 patients, combining more than 125,000 movements within our hospital with patient-related information to create time-dependent graph sequences and applied graph neural networks (GNNs) to classify patients as VRE carriers or noncarriers. …”
Get full text

Graph analysis of resting state functional brain networks and associations with cognitive outcomes in survivors of pediatric brain tumor by Eric S. Semmel, Vince D. Calhoun, Frank Hillary, Robin Morris, Tricia Z. King

“…The present study used graph theory to examine functional network properties in this population and whether graph metrics relate to core cognitive skills: attention, working memory, and processing speed. 31 survivors and 31 matched controls completed neuropsychological testing and functional magnetic resonance imaging. …”
Get full text

Graph neural networks-based prediction of drug gene association of P2X receptors in periodontal pain by Pradeep Kumar Yadalam, Prabhu Manickam Natarajan, Seyed Ali Mosaddad, Artak Heboyan

“…Notably, P2X receptors such as P2X3 and P2X7 are pivotal in dental pain pathways, making them promising targets for novel analgesic interventions. Leveraging graph neural networks (GNNs) presents an innovative approach to model graph data, aiding in the identification of drug targets and prediction of their efficacy, complementing advancements in genomics and proteomics for therapeutic development. …”
Get full text

DAPNet: multi-view graph contrastive network incorporating disease clinical and molecular associations for disease progression prediction by Haoyu Tian, Xiong He, Kuo Yang, Xinyu Dai, Yiming Liu, Fengjin Zhang, Zixin Shu, Qiguang Zheng, Shihua Wang, Jianan Xia, Tiancai Wen, Baoyan Liu, Jian Yu, Xuezhong Zhou

“…Abstract Background Timely and accurate prediction of disease progress is crucial for facilitating early intervention and treatment for various chronic diseases. …”
Get full text

Amogel: a multi-omics classification framework using associative graph neural networks with prior knowledge for biomarker identification by Chia Yan Tan, Huey Fang Ong, Chern Hong Lim, Mei Sze Tan, Ean Hin Ooi, KokSheik Wong

“…Nevertheless, relying on limited prior knowledge in generating gene graphs might lead to less accurate classification due to undiscovered significant gene-gene interactions, which may require expert intervention and can be time-consuming. …”
Get full text

Garbage in garbage out? Impacts of data quality on criminal network intervention by Wang Ngai Yeung, Riccardo Di Clemente, Renaud Lambiotte

“…Moreover, there is thus far no comprehensive understanding of the impacts of data quality on the downstream effectiveness of interventions. This work investigates the relationship between data quality and intervention effectiveness based on classical graph theoretic and machine learning-based targeting approaches. …”
Get full text

A bibliometric analysis of the current state of research on family interventions for ASD by Xuhui Meng, Xuhui Meng, Xuhui Meng, Xuhui Meng, Xue Zhou, Xue Zhou, Xue Zhou, Xue Zhou, Jiahui Luo, Jiahui Luo, Jiahui Luo, Jiahui Luo, Jiaxia Li, Jiaxia Li, Jiaxia Li, Jiaxia Li, Li Zhou, Li Zhou, Li Zhou, Li Zhou, Yuhan Zhang, Yuhan Zhang, Yuhan Zhang, Yuhan Zhang

“…According to the timeline graph, it can be learned that the current research hotspots in this field are mostly focused on early intervention in family-based, psychological stress in parents of children with autism.ConclusionThis visual analysis identifies the most influential institutions and countries, as well as cited journals and authors in the field of family therapy autism research. …”
Get full text

Suppressing the Endogenous Negative Influence Through Node Intervention in Social Networks by Satoshi Furutani, Tatsuhiro Aoshima, Toshiki Shibahara, Mitsuaki Akiyama, Masaki Aida

“…We formulate this problem as a combinatorial optimization problem on graphs. We prove that this problem is NP-hard and propose approximation algorithms to identify optimal intervention nodes that minimize the negative influence. …”
Get full text

Discovery of novel TACE inhibitors using graph convolutional network, molecular docking, molecular dynamics simulation, and Biological evaluation. by Muhammad Yasir, Jinyoung Park, Eun-Taek Han, Jin-Hee Han, Won Sun Park, Mubashir Hassan, Andrzej Kloczkowski, Wanjoo Chun

“…Using RDKit, a cheminformatics toolkit, we extracted molecular features from these compounds. We applied the GraphConvMol model within the DeepChem framework, which utilizes graph convolutional networks, to build a predictive model based on the DUD-E datasets. …”
Get full text

Dynamic climate graph network and adaptive climate action strategy for climate risk assessment and low-carbon policy responses by Fang Zhou, Yan Shi, Pengfei Zhao, Zhengzhao Gu, Ye Li

“…DCGN utilizes graph-based learning to model spatial dependencies and temporal feature extraction to analyze evolving climate patterns. …”
Get full text

Outlier detection method based on K-means by Liu Daojun, Liu Shuai, Zhang Yusong, Ou Sicheng

“…In other words, the probability distribution graph replaces expert information and assists the user in identifying valid anomalies when the distribution as well as the cause is unknown.…”
Get full text