Results 211 to 220 of about 13,735 (297)

Artificial Intelligence for Identifying Tumor‐Reactive CD8+ T Cells: Biological Principles, Computational Advances, and Future Directions

Med Research, EarlyView.
This review details a three‐stage paradigm shift for tumor‐reactive CD8+ T‐cell identification: decoding transcriptomic states, deciphering clonal functional efficacy, and molecular‐level therapeutic TCR design. Addressing translational hurdles and generative AI “scientific blind spots”—such as missing catch bonds—we present a visionary roadmap.
Chao Yang, Yingying Sun, Yan Zhang, Yifen Shi, Zhiwen Shi   +4 more
wiley   +1 more source

A new hybrid model for improving outlier detection using combined autoencoder and variational autoencoder. [PDF]

Sci Rep
Daoud AM, Elkomy OM, Khedr WI, Hosny KM.
europepmc   +1 more source

AI‐Driven Synthesis in Medicinal Chemistry: Integrating Large Language Models, Robotic Automation, and Sustainability Metrics to Accelerate Drug Discovery

Medicinal Research Reviews, EarlyView.
ABSTRACT Artificial intelligence (AI) is transforming synthetic chemistry from task‐specific predictors into integrated platforms that unify retrosynthesis, reaction optimization, and closed‐loop robotic automation. This review highlights how AI‐assisted planning and robotic execution shorten cycle times, reduce step counts, and improve route ...
Amit Gangwal, Antonio Lavecchia
wiley   +1 more source

Reconstructing 12-lead ECG from 3-lead ECG using variational autoencoder to improve cardiac disease detection of wearable ECG devices. [PDF]

PLOS Digit Health
Guan X, Wang H, Lai Y, Jin J, Li J, Zhao Q, Zhang D, Geng S, Mu G, Wang Y, Wu R, Hong S.   +11 more
europepmc   +1 more source

Physics‐Informed Generative Machine Learning for Designing Crack‐Free γ′‐Strengthened Ni‐Based Superalloys for Laser Powder Bed Fusion

Materials Genome Engineering Advances, EarlyView.
This research proposes a physics‐informed generative machine learning framework to design SHA800, a crack‐free γ′‐strengthened nickel‐based superalloy for laser powder bed fusion, achieving a 43% γ′ volume fraction and 587 HV0.2 hardness. ABSTRACT Fabricating γ′‐strengthened nickel‐based superalloys via laser powder bed fusion (LPBF) faces significant ...
Kai Guo, Dongwei Guan, Tao Hu, Yaqing Hou, Xiaoqun Li, Zhongnan Bi, Songzhe Xu, Hao Yu, Wei Xu, Weidong Xuan, Chaoyue Chen, Zhongming Ren   +11 more
wiley   +1 more source

ProT-VAE: Protein Transformer Variational AutoEncoder for functional protein design. [PDF]

Proc Natl Acad Sci U S A
Sevgen E, Moller J, Lange A, Parker J, Quigley S, Mayer J, Srivastava P, Gayatri S, Hosfield D, Dilks C, Buchanan C, Speltz T, Korshunova M, Livne M, Gill M, Ranganathan R, Costa AB, Ferguson AL.   +17 more
europepmc   +1 more source

Generative MR Multitasking With Complex‐Harmonic Cardiac Encoding: Bridging the Gap Between Gated Imaging and Real‐Time Imaging

Magnetic Resonance in Medicine, EarlyView.
ABSTRACT Purpose To develop a unified image reconstruction framework that bridges real‐time and gated cardiac MRI, including quantitative MRI. Methods We introduce generative multitasking, which learns subject‐ and dataset‐specific implicit neural temporal bases from sequence timings and an interpretable latent space for cardiac and respiratory motion.
Xinguo Fang, Anthony G. Christodoulou
wiley   +1 more source

AI-driven feature recognition of SEM profiles in deep reactive ion etching based on physics-constrained variational autoencoder. [PDF]

Microsyst Nanoeng
Wang F, Yu H, Miao Y, Sun K, Sun Y, Yang H, Li X.   +6 more
europepmc   +1 more source

Fault Detection of Electric Motors via Symmetrized Dot Pattern‐Based Features

International Journal of Mechanical System Dynamics, EarlyView.
ABSTRACT This study proposes a novel symmetrized dot pattern (SDP) approach using designed SDP‐based features, extracted from transformed vibration signals, for fault detection. These features describe the compactness, inclination, and shape of the “snowflake” diagram distributions.
Mario Spirto, Pierangelo Malfi, Armando Nicolella, Francesco Melluso, Chiara Cosenza, Sergio Savino, Vincenzo Niola   +6 more
wiley   +1 more source

Transformer and graph variational autoencoder to identify microenvironments: A deep learning protocol for spatial transcriptomics. [PDF]

STAR Protoc
Paniagua K, Huang Y, Gao SJ, Chen Y, Jin YF, Flores M.   +5 more
europepmc   +1 more source