Results 231 to 240 of about 549,786 (309)

Scalable Graph Self-Supervised Learning

In regularization Self-Supervised Learning (SSL) methods for graphs, computational complexity increases with the number of nodes in graphs and embedding dimensions. To mitigate the scalability of non-contrastive graph SSL, we propose a novel approach to reduce the cost of computing the covariance matrix for the pre-training loss function with volume ...
Pasand, Ali Saheb, Moravej, Reza, Biparva, Mahdi, Karimi, Raika, Ghodsi, Ali   +4 more
openaire   +1 more source

Real‐Time Digital Micromotor Tracking‐Enabled Ultrasensitive Immunoassay

Advanced Science, EarlyView.
An AI‐facilitated real‐time digital micromotor tracking‐enabled immunoassay is developed, which leverages a self‐developed multi‐microparticle tracking system to monitor micromotor motion trajectories in a real‐time manner for digital protein analysis.
Jingjing Shi, Zuhua Yu, Hui Tian, Wenjiao Fan, Yuanyuan Sun, Wei Ren, Chenghui Liu   +6 more
wiley   +1 more source

Rapid Proteome‐Wide Discovery of Protein–Protein Interactions With ppIRIS

Advanced Science, EarlyView.
ppIRIS is a lightweight deep learning framework for proteome‐wide protein–protein interaction prediction directly from sequence. By fusing evolutionary and structural embeddings with a regularized Siamese architecture, ppIRIS achieves state‐of‐the‐art accuracy across species, enables minute‐scale screening, and reveals biologically validated bacterial ...
Luiz Felipe Piochi, Di Tang, Johan Malmström, Yasaman Karami, Hamed Khakzad   +4 more
wiley   +1 more source

From pretraining to privacy: federated ultrasound foundation model with self-supervised learning. [PDF]

NPJ Digit Med
Jiang Y, Feng CM, Ren J, Wei J, Zhang Z, Hu Y, Liu Y, Sun R, Tang X, Du J, Wan X, Xu Y, Du B, Gao X, Wang G, Zhou S, Cui S, Li Z.   +17 more
europepmc   +1 more source

Atomic Defects in Layered Transition Metal Dichalcogenides for Sustainable Energy Storage and the Intelligent Trends in Data Analytics

Advanced Science, EarlyView.
This review comprehensively summarizes the atomic defects in TMDs for their applications in sustainable energy storage devices, along with the latest progress in ML methodologies for high‐throughput TEM data analysis, offering insights on how ML‐empowered microscopy facilitates bridging structure–property correlation and inspires knowledge for precise ...
Zheng Luo, Ying Yang, Zizhen Fu, Fengqi Liu, Susu Fang, Kele Xu, Shanshan Wang   +6 more
wiley   +1 more source

Improving Coronary Artery Disease Diagnosis in Cardiac MRI with Self-Supervised Learning. [PDF]

Diagnostics (Basel)
Khalid U, Kaya M, Alhajj R.
europepmc   +1 more source

A Fully Self‐Powered Digital Wearable System for the Auxiliary Treatment of Plantar Fasciitis

Advanced Science, EarlyView.
This study reports a system‐level fully self‐powered digital wearable system (FS‐DWS) for the auxiliary treatment of plantar fasciitis. By integrating arch support, energy harvesting, wearable sensing, and machine learning‐driven closed‐loop visualized feedback, the system enables effective plantar pressure reduction and self‐powered, real‐time plantar
Jiacheng Hou, Ying Hong, Shiyuan Liu, Qiqi Pan, Jingyu Zhang, Qingyang Xu, Qiyi Nie, Zhonghe Wang, Liming Xin, Yilong Wang, Biao Wang   +10 more
wiley   +1 more source

The detection of algebraic auditory structures emerges with self-supervised learning. [PDF]

PLoS Comput Biol
Orhan P, Boubenec Y, King JR.
europepmc   +1 more source

Automated segmentation of lesions and organs at risk on [68Ga]Ga-PSMA-11 PET/CT images using self-supervised learning with Swin UNETR [PDF]

Elmira Yazdani, Najme Karamzadeh-Ziarati, Seyyed Saeid Cheshmi, Mahdi Sadeghi, Parham Geramifar, Habibeh Vosoughi, Mahmood Kazemi Jahromi, Saeed Reza Kheradpisheh   +7 more
openalex   +1 more source

Leveraging Artificial Intelligence and Large Language Models for Cancer Immunotherapy

Advanced Science, EarlyView.
Cancer immunotherapy faces challenges in predicting treatment responses and understanding resistance mechanisms. Artificial intelligence (AI) and machine learning (ML) offer powerful solutions for cancer immunotherapy in patient stratification, biomarker discovery, treatment strategy optimization, and foundation model development.
Xinchao Wu, Mengtao Sun, Lusheng Li, Jieqiong Wang, Shibiao Wan   +4 more
wiley   +1 more source