Results 111 to 120 of about 4,069,375 (260)
Advanced Science, EarlyView.A multimodal fusion framework integrating sequence, atomic, and fragment representations captures drug–target interactions across multiple scales. The model delivers strong predictive performance and enables efficient virtual screening. Applied to hematopoietic progenitor kinase 1 (HPK1), it identifies structurally diverse inhibitors with nanomolar ...
Shuo Liu +7 more
wiley +1 more source
Graph Convolutional Neural Networks for Histologic Classification of Pancreatic Cancer. [PDF]
Arch Pathol Lab Med, 2023 Wu W +4 more
europepmc +1 more source
Advanced Science, EarlyView.A novel targeted and pH‐responsive MRI contrast agent was integrated with a 3D nnU‐Net deep learning framework to enable accurate delineation of tumor boundary morphology in HER2‐positive breast cancer imaging. ABSTRACT Breast cancer continues to be a leading cause of cancer‐related mortality in women globally, where precise diagnosis and clear tumor ...
Jiaying Zheng +7 more
wiley +1 more source
Smart Logistics Model for Supply Chain Management via Brain-Inspired Geometric Deep Networks
BiomimeticsSystematic logistics plays a key role in fostering profitable development in supply chains. An intelligent logistics model can help create a more agile, sustainable, and resilient supply chain.
Mehdi Khaleghi +5 more
doaj +1 more source
Atomistic Kinetics of Dislocation‐Mediated Grain Growth in Monolayer MoS2
Advanced Science, EarlyView.Atomic‐resolution in‐situ heating microscopy directly visualizes dislocation‐mediated grain boundary migration and grain growth in monolayer MoS2. Mobile grain boundaries migrate through collective motion of Mo 5|7 dislocations, whereas S 5|7 defects remain largely immobile.
Chang‐Won Choi +11 more
wiley +1 more source
Graph convolutional neural networks for text categorization
, 2018 Text categorization is the task of labelling text data from a predetermined set of thematic labels. In recent years, it has become of increasing importance as we generate large volumes of data and require the ability to search through these vast datasets
Lakhotia, Suyash
core
Empowering Simple Graph Convolutional Networks [PDF]
Many neural networks for graphs are based on the graph convolution (GC) operator, proposed more than a decade ago. Since then, many alternative definitions have been proposed, which tend to add complexity (and nonlinearity) to the model.
Pasa, L, Navarin, N, Sperduti, A, Erb, W
core +1 more source
Disentangling Heterogeneous Molecular Networks for Multi‐Omics‐Driven Cancer Driver Discovery
Advanced Science, EarlyView.DRIVE integrates PPI topology and pan‐cancer multi‐omics profiles through dual‐view graph disentanglement, contrastive representation learning, and joint optimization. Across six PPI networks, DRIVE outperforms ten baselines and remains robust to structural and annotation perturbations.
Xinjing Gong +8 more
wiley +1 more source
DeepBindGCN: Integrating Molecular Vector Representation with Graph Convolutional Neural Networks for Protein-Ligand Interaction Prediction. [PDF]
Molecules, 2023 Zhang H, Saravanan KM, Zhang JZH.
europepmc +1 more source
Advanced Electronic Materials, EarlyView.In this work, low‐resolution infrared imaging is combined with a 28 nm FeFET IMC architecture to enable compact, energy‐efficient edge inference. MLC FeFET devices are experimentally characterized, and controlled multi‐level current accumulation is validated at crossbar array level.
Alptekin Vardar +9 more
wiley +1 more source