Results 171 to 180 of about 64,275 (248)

BiAEImpute: a robust bidirectional autoencoder framework for High-fidelity dropout imputation in single-cell transcriptomics. [PDF]

BMC Genomics
Zhang Y, Liu X, Wang Y, Wang Y.
europepmc   +1 more source

New Opportunities For the Integration of Artificial Intelligence With Materials Science: From Large Language Models to Embodied Large Models

Materials Genome Engineering Advances, EarlyView.
This review first introduces the diversified applications of large language models in materials discovery. Subsequently, the evolution of autonomous experimentation platforms empowered by large language models is analyzed. Finally, four key future research interests are proposed to develop embodied large models for driving autonomous experimentation ...
Zhen Song, Yishuai Cai, Yadong Yu, Pan Xiang, Minglong Li, Zhe Liu, Mengyan Dai   +6 more
wiley   +1 more source

Ai-guided vectorization for efficient storage and semantic retrieval of visual data. [PDF]

Discov Artif Intell
Harby AA, Zulkernine F, Abdulsalam HM.
europepmc   +1 more source

TopoMAS: Large Language Model Driven Topological Materials Multi‐Agent System

Materials Genome Engineering Advances, EarlyView.
TopoMAS is an interactive multi‐agent framework that revolutionizes topological materials discovery through human–AI collaborative intelligence. The system integrates natural language processing, knowledge retrieval from literature and databases, crystal structure generation, and automated first‐principles calculations within a unified workflow.
Baohua Zhang, Xin Li, Huangchao Xu, Zhong Jin, Quansheng Wu, Ce Li   +5 more
wiley   +1 more source

Quantum denoising autoencoder improves retinal fundus image quality for early diabetic retinopathy screening. [PDF]

Sci Rep
Chilukuri R, P P, Gatla RK, Almenweer RA.   +3 more
europepmc   +1 more source

Accelerating MRI With Longitudinally‐Informed Latent Posterior Sampling

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To accelerate MRI acquisition by incorporating the previous scans of a subject during reconstruction. Although longitudinal imaging constitutes much of clinical MRI, leveraging previous scans is challenging due to the complex relationship between scan sessions, potentially involving substantial anatomical or pathological changes, and ...
Yonatan Urman, Zachary Shah, Ashwin Kumar, Bruno P. Soares, Kawin Setsompop   +4 more
wiley   +1 more source

MASE-GC: a multi-omics autoencoder and stacking ensemble framework for gastric cancer classification. [PDF]

Front Cell Dev Biol
Liu D, Che Z, Xu G, Huang Y.
europepmc   +1 more source

Spectral Representation of Neurochemicals With Phase, Frequency Offset, and Lineshape Invariance: Application to JPRESS for In Vivo Concentration and T2 Mapping by Deep Learning

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose Using artificial intelligence neural networks to generate a representation that maps the input directly to neurochemical concentrations and metabolite‐level average transverse relaxation times (T2). Methods The proposed model used time‐domain JPRESS data as input and was trained to be invariant to phase shifts, frequency offsets, and ...
Yan Zhang, Jun Shen
wiley   +1 more source

Generating realistic artificial human genomes using adversarial autoencoders. [PDF]

NAR Genom Bioinform
Burnard C, Mancheron A, Ritchie W.
europepmc   +1 more source

Advancing Quantitative Susceptibility Mapping With 2.5D Diffusion Models for Rapid Intracranial Hemorrhage Quantification

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a generative diffusion model‐based approach for robust and efficient quantitative susceptibility mapping (QSM) reconstruction in intracranial hemorrhage (ICH), applicable to both standard gradient echo (GRE) and rapid echo planar imaging (EPI) acquisitions.
Zhuang Xiong, Yang Gao, Feng Liu, Derek Emery, Ken Butcher, Alan H. Wilman, Hongfu Sun   +6 more
wiley   +1 more source