Results 241 to 250 of about 229,000 (311)

ML Workflows for Screening Degradation‐Relevant Properties of Forever Chemicals

Advanced Science, EarlyView.
The environmental persistence of per‐ and polyfluoroalkyl substances (PFAS) necessitates efficient remediation strategies. This study presents physics‐informed machine learning workflows that accurately predict critical degradation properties, including bond dissociation energies and polarizability.
Pranoy Ray, Andrew R. Castillo, Manoj Kolel‐Veetil, Surya R. Kalidindi   +3 more
wiley   +1 more source

AugGCL: Multimodal graph learning for spatial transcriptomics analysis with enhanced gene and morphological data. [PDF]

PLoS Comput Biol
Ji T, Yang B, Wang M, Ji H, Yang H, Liu Y.   +5 more
europepmc   +1 more source

Sustainable Materials Design With Multi‐Modal Artificial Intelligence

Advanced Science, EarlyView.
Critical mineral scarcity, high embodied carbon, and persistent pollution from materials processing intensify the need for sustainable materials design. This review frames the problem as multi‐objective optimization under heterogeneous, high‐dimensional evidence and highlights multi‐modal AI as an enabling pathway.
Tianyi Xu, Tianshuo Wei, Yan Ge, Bo Peng, Yue Li, Maolin Wang, Peng Wen, Chao Yang, Ye Wei   +8 more
wiley   +1 more source

RETRACTED ARTICLE: DynaGraph: interpretable dynamic graph learning for temporal electronic health records. [PDF]

NPJ Digit Med
Mesinovic M, Molaei S, Watkinson P, Zhu T.   +3 more
europepmc   +1 more source

tBid‐Mediated Genetic Ablation of Connective Tissue Cells Reveals Their Key Regulatory Function During Limb Regeneration in Axolotls

Advanced Science, EarlyView.
We establish a tBid‐mediated cell ablation system in axolotls, achieve rapid and efficient ablation of multiple cell types, including muscle stem cell, spinal cord cell, and connective tissue (CT) cells. We investigate the role of CT using tBid‐mediated CT ablation and identify its essential role for limb development and regeneration.
Yan Hu, Weimin Feng, Zitian He, Jiayi Zeng, Jingyi Tang, Shulin Li, Tongbo Yu, Sulei Fu, Hui Ma, Binbin Lu, Yanmei Liu, Ji‐Feng Fei   +11 more
wiley   +1 more source

TCRLens: structure-aware equivariant graph learning for TCR-pMHC-I recognition and immunogenic epitope discovery. [PDF]

Bioinform Adv
Siriarchawatana P, Ingsriswang S, Kaewborisuth C, Jongkaewwattana A.   +3 more
europepmc   +1 more source

Unifying Composition and Process Design: A Heterogeneous Graph Neural Network for Discovering High‐Performance Cu Alloys

Advanced Science, EarlyView.
By overcoming the fixed‐path limitations of conventional machine learning, a heterogeneous graph neural network fundamentally reconstructs material data representation. Integrating variable processing sequences with intrinsic elemental features, this framework enables exploratory optimization across high‐dimensional spaces.
Jie Yin, Qian Lei, Wei Yan, Yue Li, Tong Xie, Shuang Zhou, Chong Wang, Bram Hoex, Qiang Long, Caoyang Jiang, Min Song, Zhou Li, Zhangwei Wang   +12 more
wiley   +1 more source

Vector-guided graph learning for spatial multi-slice multi-omics alignment. [PDF]

Cell Rep Methods
Lou Y, Li X, Yang Q, Dai H, Ma K, Zuo C.
europepmc   +1 more source

Efficient Screening of Organic Singlet Fission Molecules Using Graph Neural Networks

Advanced Science, EarlyView.
A high‐throughput screening framework based on graph neural networks (GNNs) and multi‐level validation facilitates the identification of singlet fission (SF) candidates. By efficiently predicting excitation energies across 20 million molecules, and integrating TDDFT calculations, synthetic accessibility assessments, and GW+BSE calculations, this ...
Li Fu, Longfei Lv, Fan Zhang, Si Zhou, Weiwei Gao, Jijun Zhao   +5 more
wiley   +1 more source

DSS-PPI: a self-supervised graph learning framework for protein-protein interaction prediction via multimodal sequence semantics. [PDF]

BMC Genomics
Li S, Wang Y, Zhang H, Xing X, Shao Y, Qi Z.   +5 more
europepmc   +1 more source