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Found 12 papers, 3 papers with code
SGAC: A Graph Neural Network Framework for Imbalanced and Structure-Aware AMP Classification
Classifying antimicrobial peptides(AMPs) from the vast array of peptides mined from metagenomic sequencing data is a significant approach to addressing the issue of antibiotic resistance.
Cross-Attention Graph Neural Networks for Inferring Gene Regulatory Networks with Skewed Degree Distribution
However, most of these studies have not considered the skewed degree distribution of genes.
A Decade of Deep Learning: A Survey on The Magnificent Seven
Deep learning has fundamentally reshaped the landscape of artificial intelligence over the past decade, enabling remarkable achievements across diverse domains.
DuSEGO: Dual Second-order Equivariant Graph Ordinary Differential Equation
Graph Neural Networks (GNNs) with equivariant properties have achieved significant success in modeling complex dynamic systems and molecular properties.
Degree Distribution based Spiking Graph Networks for Domain Adaptation
The proposed DeSGDA addresses the spiking graph domain adaptation problem by three aspects: node degree-aware personalized spiking representation, adversarial feature distribution alignment, and pseudo-label distillation.
Dynamic PDB: A New Dataset and a SE(3) Model Extension by Integrating Dynamic Behaviors and Physical Properties in Protein Structures
Despite significant progress in static protein structure collection and prediction, the dynamic behavior of proteins, one of their most vital characteristics, has been largely overlooked in prior research.
CL4CTR: A Contrastive Learning Framework for CTR Prediction
Many Click-Through Rate (CTR) prediction works focused on designing advanced architectures to model complex feature interactions but neglected the importance of feature representation learning, e. g., adopting a plain embedding layer for each feature, which results in sub-optimal feature representations and thus inferior CTR prediction performance.
Enhancing CTR Prediction with Context-Aware Feature Representation Learning
However, most methods only learn a fixed representation for each feature without considering the varying importance of each feature under different contexts, resulting in inferior performance.
TB-ICT: A Trustworthy Blockchain-Enabled System for Indoor COVID-19 Contact Tracing
Recently, as a consequence of the COVID-19 pandemic, dependence on Contact Tracing (CT) models has significantly increased to prevent spread of this highly contagious virus and be prepared for the potential future ones.
On the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems (SAS)
Symbiotic Autonomous Systems (SAS) are advanced intelligent and cognitive systems exhibiting autonomous collective intelligence enabled by coherent symbiosis of human-machine interactions in hybrid societies.
Diagnosis/Prognosis of COVID-19 Images: Challenges, Opportunities, and Applications
This has resulted in a surge of interest to develop Radiomics models for analysis and interpretation of medical images.
Dynamic Multi-path Neural Network
Although deeper and larger neural networks have achieved better performance, the complex network structure and increasing computational cost cannot meet the demands of many resource-constrained applications.
