Results 111 to 120 of about 9,194 (254)

Impact of Single‐Cell RNA Reference Selection for the Deconvolution of Breast Cancer Spatial Transcriptomics Datasets

International Journal of Cancer, EarlyView.
ABSTRACT Spot‐based spatial transcriptomics (ST) allows for unbiased gene expression analysis within tissue architecture, overcoming the limitations of single‐cell RNA sequencing (scRNA‐seq) by preserving spatial context. However, the high spatial resolution in ST leads to cellular heterogeneity within spots, requiring computational deconvolution to ...
Stefan Altendorfer, Scott J. Walker, Carsten O. Daub   +2 more
wiley   +1 more source

Drug-target interaction prediction based on graph convolutional autoencoder with dynamic weighting residual GCN

BMC Bioinformatics
Background The exploration of drug-target interactions (DTIs) is a critical step in drug discovery and drug repurposing. Recently, network-based methods have emerged as a prominent research area for predicting DTIs. These methods excel by extracting both
Ming Zeng, Min Wang, Fuqiang Xie, Zhiwei Ji   +3 more
doaj   +1 more source

Promises and limitations of deep learning for predicting knee osteoarthritis progression from medical imaging: A systematic review

Knee Surgery, Sports Traumatology, Arthroscopy, EarlyView.
Abstract Purpose To systematically evaluate the performance, methodological quality, and translational barriers of deep learning (DL) models for predicting knee osteoarthritis (KOA) progression from medical imaging. Methods Following PRISMA guidelines, we searched PubMed, Scopus, and Web of Science (inception to June 2026) for peer‐reviewed studies ...
Amna Gillani, Rüdiger von Eisenhart‐Rothe, Klaus Woertler, Michael T. Hirschmann, Daniel Rueckert, Florian Hinterwimmer   +5 more
wiley   +1 more source

DVAE-GNN: a dual variational autoencoder graph neural network for unsupervised anomaly detection in static attributed networks

Discover Data
Unsupervised anomaly detection in static attributed networks is a crucial research area in network science, with applications spanning cybersecurity, social network analysis, and beyond.
Hari Prasad Piridi, Dipanjan Chakraborty, Prajna Devi Upadhyay   +2 more
doaj   +1 more source

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

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

Norm Augmented Graph AutoEncoders for Link Prediction

ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Link Prediction (LP) is a crucial problem in graph-structured data. Graph Neural Networks (GNNs) have gained prominence in LP, with Graph AutoEncoders (GAEs) being a notable representation. However, our empirical findings reveal that GAEs' LP performance suffers heavily from the long-tailed node degree distribution, i.e., low-degree nodes tend to ...
Liu, Yunhui, Zhang, Huaisong, Gao, Xinyi, Guo, Liuye, Tao, Zhen, He, Tieke   +5 more
openaire   +6 more sources

Artificial Intelligence‐Driven Natural Product Drug Discovery: From Computational Genome Mining to Clinical Translation

Medicinal Research Reviews, EarlyView.
ABSTRACT Natural products (NPs) have historically yielded numerous therapeutic agents, yet their integration into modern drug discovery has been constrained by chemical complexity, low abundance, laborious dereplication, and limited target annotation.
Antonio Lavecchia
wiley   +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

Variational autoencoder-based spatio-temporal disentanglement for link prediction in dynamic graph

Complex & Intelligent Systems
Link prediction in dynamic graphs models real-world dynamic networks, providing a concrete and insightful representation of various scenarios. Despite recent advancements in dynamic graph learning, the factorized representations of features across ...
Peng You, Shurui Xu, Jingran Wu, Xiaohu Zhao, Jiapin Ren   +4 more
doaj   +1 more source