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Found 8 papers, 1 papers with code
Predicting Molecule-Target Interaction by Learning Biomedical Network and Molecule Representations
Most existing methodologies utilize either biomedical network information or molecule structural features to predict potential interaction link.
Ligandformer: A Graph Neural Network for Predicting Compound Property with Robust Interpretation
Robust and efficient interpretation of QSAR methods is quite useful to validate AI prediction rationales with subjective opinion (chemist or biologist expertise), understand sophisticated chemical or biological process mechanisms, and provide heuristic ideas for structure optimization in pharmaceutical industry.
Sequence-based deep learning antibody design for in silico antibody affinity maturation
Traditional in vitro approaches use hybridoma or phage display for candidate selection, and surface plasmon resonance (SPR) for evaluation, while in silico computational approaches aim to reduce the high cost and improve efficiency by incorporating mathematical algorithms and computational processing power in the design process.
Real-time tracking of COVID-19 and coronavirus research updates through text mining
The novel coronavirus (SARS-CoV-2) which causes COVID-19 is an ongoing pandemic.
Enhance Information Propagation for Graph Neural Network by Heterogeneous Aggregations
Graph neural networks are emerging as continuation of deep learning success w. r. t.
Subjective and Objective Visual Quality Assessment of Textured 3D Meshes
Based on analysis of the results, we propose two new metrics for visual quality assessment of textured mesh, as optimized linear combinations of accurate geometry and texture quality measurements.
Heterogeneous Graph based Deep Learning for Biomedical Network Link Prediction
Multi-scale biomedical knowledge networks are expanding with emerging experimental technologies that generates multi-scale biomedical big data.
Locally Adaptive Learning Loss for Semantic Image Segmentation
We propose a novel locally adaptive learning estimator for enhancing the inter- and intra- discriminative capabilities of Deep Neural Networks, which can be used as improved loss layer for semantic image segmentation tasks.
