Results 151 to 160 of about 238,312 (311)

Patient-Specific Semi-Supervised Learning for Postoperative Brain Tumor Segmentation

, 2014
In contrast to preoperative brain tumor segmentation, the problem of postoperative brain tumor segmentation has been rarely approached so far. We present a fully-automatic segmentation method using multimodal magnetic resonance image data and patient ...
Slotboom, Johannes, Mauricio Reyes, Stefan Bauer, Meier, Raphael, Wiest, Roland, Reyes, Mauricio, Raphael Meier, Johannes Slotboom, Bauer, Stefan, Roland Wiest   +9 more
core   +1 more source

How Advanced Artificial Intelligence Technologies Shape Drug–Drug and Drug–Target Interaction Modeling

Advanced Science, EarlyView.
This review explores the convergence of artificial intelligence technologies in modeling drug–drug and drug–target interactions. By evaluating advanced feature engineering, architectural innovations, and learning paradigms reveals shared evolutionary trends and critical challenges, such as cold‐start settings and shortcut learning.
Xin Sun, Tong Wang
wiley   +1 more source

Full‐Stack Architectures for Intelligent Brain‐Computer Interfaces

Advanced Science, EarlyView.
System‐level overview of brain–computer interfaces (BCIs), illustrating the integration of neural signal acquisition, wireless transmission, and adaptive decoding. Advanced electrode, tissue interfaces, energy‐efficient communication, and robust algorithms collectively enable stable signal quality, real‐time processing, and closed‐loop operation ...
Hee Kyu Lee, Hyun Bin Kim, Sang Uk Park, Janghoon Joo, Jinhong Min, Geumbee Lee, Joohoon Kang, Hyoyoung Jeong, Jae‐Young Yoo, Sang Min Won   +9 more
wiley   +1 more source

PhosSight: A Unified Deep Learning Framework Boosting and Accelerating Phosphoproteome Identification to Enable Biological Discoveries

Advanced Science, EarlyView.
PhosSight is a unified deep‐learning framework for phosphoproteome identification, featured by a phosphorylation‐aware detectability predictor. It improves identification sensitivity in DDA through deep re‐localization and rescoring, accelerates DIA searches by detectability‐guided spectral library pruning, and expands phosphoproteome coverage to ...
Ben Wang, Zhiyuan Cheng, Chengying She, Hongwei Zhao, Jiahui Zhang, Lin Lv, Zhihao Yan, Hongwen Zhu, Lizhuang Liu, Yan Fu, Xinpei Yi   +10 more
wiley   +1 more source

Photonic‐Enabled Energy‐Efficient Transparent Neuromorphic Computing Devices: A Review

Advanced Science, EarlyView.
Transparent photonic neuromorphic computing devices merge optics and brain‐inspired computing to overcome von Neumann bottlenecks with ultrafast, low‐energy processing. By exploiting transparent oxides, 2D materials, phase‐change materials, and hybrid heterostructures, these platforms enable photonic synapses, memory, and logic for see‐through edge ...
Shuvaraj Ghosh, Ki‐bum Lee, Junghyeon Lee, Seunghee Cho, Malkeshkumar Patel, Hangfei Li, Yu Wen, Ye Zhou, Joondong Kim   +8 more
wiley   +1 more source

Multiview Semi-Supervised Learning for Ranking Multilingual Documents

, 2011
International audienceWe address the problem of learning to rank documents in a multilingual context, when reference ranking information is only partially available. We propose a multiview learning approach to this semi-supervised ranking task, where the
Usunier, Nicolas, Massih-Reza Amini, Cyril Goutte, Goutte, Cyril, Amini, Massih-Reza, Nicolas Usunier   +5 more
core   +1 more source

AI‐Designed Cyclic Peptides Enable Controllable Modulation of the CD28 Immune Checkpoint

Advanced Science, EarlyView.
AI‐designed cyclic peptides enable controllable modulation of the CD28 immune checkpoint through reversible disruption of CD28‐CD80/CD86 interactions. The lead peptide, CIP‐3, suppresses T‐cell activation without intrinsic agonist activity, demonstrates dose‐dependent efficacy in a murine colitis model, and attenuates inflammatory cytokine production ...
Katarzyna Kuncewicz, Saurabh Upadhyay, Renjie Zhu, Hongliang Duan, Moustafa T. Gabr   +4 more
wiley   +1 more source

spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R

In this paper, we present an R package that combines feature-based (X) data and graph-based (G) data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled) and missing for the remainder ...
Mark Culp
core  

Machine Learning‐Driven Prediction of Microplastic Aging Processes and Environmental Risk Assessment Across Multi‐Media Systems

Advanced Science, EarlyView.
This perspective proposes a cohesive machine learning strategy to decode microplastic aging. It advocates for Federated Learning to dismantle global data silos and introduces the TRACE framework (TRansport, Aging, Corona, Ecotoxicity). By integrating physics‐informed modeling with causal discovery, this approach bridges the laboratory‐field gap to ...
Yaping Lyu, Xinran Qiu, Xing Li, Tianhuan Yang, Xuetao Guo, Hao Qiu, Peng Zhang   +6 more
wiley   +1 more source

Semi-supervised Learning with Deep Generative Models

, 2015
The ever-increasing size of modern data sets combined with the difficulty of obtaining label information has made semi-supervised learning one of the problems of significant practical importance in modern data analysis.
Kingma, D.P., Weinberger, K.Q., Mohamed, S., Ghahramani, Z., Lawrence, N.D., Welling, M., Cortes, C., Rezende, D.J.   +7 more
core